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  • Published: 27 April 2021

Combining ambitious climate policies with efforts to eradicate poverty

  • Bjoern Soergel   ORCID: 1 ,
  • Elmar Kriegler   ORCID: 1 , 2 ,
  • Benjamin Leon Bodirsky   ORCID: 1 ,
  • Nico Bauer   ORCID: 1 ,
  • Marian Leimbach   ORCID: 1 &
  • Alexander Popp 1  

Nature Communications volume  12 , Article number:  2342 ( 2021 ) Cite this article

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  • Climate-change mitigation
  • Climate-change policy
  • Sustainability

Climate change threatens to undermine efforts to eradicate extreme poverty. However, climate policies could impose a financial burden on the global poor through increased energy and food prices. Here, we project poverty rates until 2050 and assess how they are influenced by mitigation policies consistent with the 1.5 °C target. A continuation of historical trends will leave 350 million people globally in extreme poverty by 2030. Without progressive redistribution, climate policies would push an additional 50 million people into poverty. However, redistributing the national carbon pricing revenues domestically as an equal-per-capita climate dividend compensates this policy side effect, even leading to a small net reduction of the global poverty headcount (−6 million). An additional international climate finance scheme enables a substantial poverty reduction globally and also in Sub-Saharan Africa. Combining national redistribution with international climate finance thus provides an important entry point to climate policy in developing countries.

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With the adoption of the Paris Agreement and the Sustainable Development Goals (SDGs) an ambitious agenda for mitigating climate change, fostering human development and protecting the biosphere has been set by the international community. Its implementation requires climate policies to go hand in hand with broader sustainable development objectives 1 , 2 , 3 , 4 , 5 .

Arguably one of the most important targets of this agenda is to eradicate extreme poverty as measured by a daily income below the international poverty threshold (SDG 1.1). However, the impacts of unabated climate change could undermine the efforts to eradicate poverty 6 . Negative economic impacts from increased temperatures would affect countries of the Global South more severely 7 , 8 , leading to an increase in global inequality 9 . Within a given country, poorer households are also more vulnerable to climate impacts 10 , 11 .

The importance of eradicating poverty is also explicitly recognized in the Paris Agreement. Notably, ending extreme poverty would only marginally increase the efforts required to meet mitigation targets 12 . Nonetheless, also mitigation policies could have negative side effects for the global poor. At the international level, a uniform carbon price would lead to higher relative policy costs for developing countries 13 , 14 . Without compensating measures mitigation policies could also hamper progress towards universal access to clean energy 15 , 16 , thus potentially preventing further development and creating a poverty trap 17 , 18 . Similarly, higher food prices caused by land-based mitigation measures 19 , 20 , 21 , 22 could undermine efforts towards a world without hunger 23 , 24 , 25 .

Quantifying the poverty implications of climate change and mitigation policies requires capturing the heterogeneity within countries 10 , 26 , 27 . Although these distributional effects are of key importance 28 , 29 , 30 , so far most integrated assessment models (IAMs)—the major tools for analysing climate policies—do not represent them 27 . At the same time, the existing empirical literature on distributional effects of climate policies within individual countries (e.g. 31 ) lacks the global context required for the analysis of mitigation pathways consistent with the climate goals of the Paris Agreement.

Previous studies considering multiple countries have focused on the poverty implications of moderate carbon prices 32 , 33 , but are limited to a static perspective and/or a moderate number of countries 32 , or do not include the important effect of land-based mitigation measures on poverty 33 . An analysis of the poverty consequences of the Nationally Determined Contributions (NDCs) 34 has shown relatively moderate effects on global poverty in 2030. By contrast, our study quantifies the consequences of an ambitious, Paris-compatible mitigation pathway for global poverty until mid-century. As such, we provide an assessment of the potential trade-off between climate action (SDG 13) and poverty eradication (SDG 1), and show how it can be overcome.

Based on a mitigation pathway computed with the state-of-the-art IAM framework REMIND-MAgPIE 35 , we compute the resulting changes in the income distribution and the effects on national, regional and global poverty rates. We focus on a scenario with burden sharing through internationally differentiated carbon prices. National redistribution policies are funded from the domestic carbon pricing revenue; we highlight the effect of different redistribution schemes on poverty outcomes. We also explore the effects of international climate finance on poverty alleviation.

Poverty trends in reference scenarios

The development of extreme poverty as measured by the international poverty line of 1.90$/day (PPP 2011) depends strongly on future socioeconomic development. Here we follow the Shared Socioeconomic Pathways (SSPs 36 ) in our assumptions for GDP, population and inequality trends. Using the middle-of-the-road pathway SSP2, and in the absence of climate impacts or mitigation policies, we project a continued reduction of extreme poverty. Nonetheless we find that around 350 million people (uncertainty range: 308–411 million) will remain in absolute poverty in 2030, the large majority of them in Sub-Saharan Africa (Fig.  1 ). Therefore the target to eradicate poverty by 2030 (SDG 1.1) will be missed if socioeconomic development continues in accordance with recent historical trends.

figure 1

a Projections for national poverty rates in an SSP2 scenario in 2030. For countries that are greyed out in the map, no data was available to calibrate the model for poverty outcomes. b Global poverty headcount in the different SSP reference scenarios. While SSP3 and SSP4 are very similar in the global trend, they differ in national and regional poverty projections. The solid/dashed/dotted lines indicate the central projection of our model for the respective SSP scenario. The shaded bands are the 68% prediction intervals; uncertainties are calculated from the regression model for the logit-transformed country-level poverty rates, and propagated to the global poverty headcount (see Section ‘Projecting poverty headcounts and uncertainties’ in Methods). Recent historical values 73 are shown with black dots.

In the SSP1 and SSP5 scenarios with high income growth and decreasing levels of inequality, poverty is reduced at a faster pace. But even under these optimistic socioeconomic assumptions we project around 190 million (SSP5) and 230 million (SSP1) people remaining in extreme poverty in 2030. In the more pessimistic scenarios SSP3 and SSP4 the reduction of poverty slows down, leading to nearly 500 million people in extreme poverty in 2030 in both scenarios. Qualitatively very similar results, but higher overall poverty projections across all SSPs, are also reported by Crespo Cuaresma et al. 37 . Even regardless of the effects of climate change and mitigation policies, these findings mandate substantially increased efforts towards eradicating extreme poverty.

Looking further ahead, we project around 90 million people remaining in extreme poverty by 2050 in the SSP2 reference scenario. The different scenarios span a range of 10–20 million people (SSP5, SSP1) to around 400 million people (SSP3, SSP4). Note, however, that our projections do not include the impacts of unabated climate change on poverty rates, which would likely increase poverty headcounts considerably, especially in the longer term.

Effects of climate policy and redistribution

We focus on ambitious mitigation policies consistent with the 1.5  ∘ C target, implemented through a carbon price. The initial price level is differentiated by regions to model a period of staged accession. Developing regions initially face low carbon prices, but converge to the price level of industrialized regions by 2050 (see Methods and Supplementary Fig.  2 for details). To span the range of different mitigation challenges depending on the socioeconomic and technological baseline, we compute mitigation pathways for the three SSPs implemented in the REMIND-MAgPIE framework: the middle-of-the-road pathway SSP2, the fossil-fuel driven development pathway SSP5, and the more sustainable pathway SSP1. We find that in all three scenarios, mitigation policies without associated redistribution policies would lead to an increase in poverty compared to the baseline trend. However, we show that already a progressive redistribution of the national carbon pricing revenues can substantially alleviate or even compensate this policy side effect.

If the revenues are used in a distributionally neutral way, i.e. without changing the level of inequality, richer households accrue a substantial part of the revenues, while low-income households are only partly compensated for their higher expenditures for energy and food. As a result, we project a substantial increase in poverty rates, most prominently in Sub-Saharan Africa, but to a lesser extent also in India, Latin America and South-East Asia (example for SSP2 in Fig.  2 a).

figure 2

We show here our projections for the difference between policy and baseline poverty rates for the two different revenue recycling schemes. While climate policy without associated progressive redistribution (panel a , ‘neutral’) would lead to a substantial increase in poverty rates, this policy side effect could be reduced or largely overcome by redistributing the associated carbon pricing revenue (panel b , ‘progressive’). Light grey lines show national borders, while solid black lines delineate the model regions used in REMIND-MAgPIE.

If, on the other hand, the carbon pricing revenues are redistributed in a progressive way (implemented as an equal-per-capita climate dividend), the side effects of mitigation policies on poverty are substantially reduced. In almost all countries outside of Sub-Saharan Africa they are even fully compensated, leading to similar poverty rates as in the baseline scenario or a net reduction of poverty (Fig.  2 b). The combination of these two policies could therefore alleviate or even overcome the trade-off between mitigation of climate change and poverty eradication, thus providing an important entry point to climate policy in developing countries.

Note that in our main analysis we apply this progressive redistribution only to revenues from the energy system, as costs for implementation and monitoring can be expected to absorb a large part of the revenues from pricing land-use emissions. While our distributional analysis and the calculation of poverty rates are performed at the country level, the mitigation pathways are downscaled from coarser regional results (see Methods section for details). As such our results at the national level capture country-specific socioeconomic trends, but do not take into account differences in energy system characteristics or fossil-fuel endowments between countries belonging to the same model region. Therefore we caution against interpreting these results as detailed country-level case studies, and focus on global and regional trends for the remainder of this paper.

Global poverty headcount

We show in Fig.  3 the globally aggregated poverty headcount, both for the SSP2 baseline scenario and the SSP2 policy scenario with the two different redistribution schemes described above. The additional number of people in poverty by 2030 (i.e. the difference between policy and baseline results), both globally and for the four world regions most relevant for the global poverty headcount, is displayed in Fig.  4 for all three SSP mitigation scenarios we consider.

figure 3

We show here our projections for the global number of people below the absolute poverty threshold of 1.90 $/day (note the logarithmic scale). The SSP2 baseline (black line) is identical to Fig.  1 . For the climate policy scenario the poverty outcomes depend substantially on how the carbon pricing revenue is used (coloured lines) — leading either to a slowdown of poverty reduction or to a trend comparable to the baseline. The solid line represents the central projection of our model; the shaded bands are the 68% prediction intervals (see also caption of Fig.  1 ).

figure 4

Both at the global level, and for the four most relevant regions, we show the difference between policy and baseline results for SSP1, SSP2 and SSP5. Again we differentiate between the two revenue recycling scenarios. The bars represent the central projection of our model; error bars are the 68% prediction intervals for the difference between the respective policy scenario and the baseline case (see Section ‘Projecting poverty headcounts and uncertainties’ in Methods).

In the case of climate policy without associated progressive redistribution (‘neutral’) we project an additional 50 million people in extreme poverty in SSP2 by 2030. If, however, the entire domestic carbon pricing revenue is redistributed progressively, the negative side effects of climate policy on poverty eradication can be completely compensated (−6 million people globally). This is an encouraging result: although the implementation of climate policies in developing countries would put a substantial burden especially on the poorest households, already the domestic revenues generated from carbon pricing are sufficient to offset the negative side effect on poverty eradication, at least at the global level. Hence, the reduction of total, national economic income through carbon pricing does not necessarily increase poverty if the revenues are recycled on an equal-per-capita basis (see also Section  7 of Supplementary Information).

This finding also holds for scenarios with different mitigation challenges. In SSP5, mitigation pressure is highest, and thus the increase in poverty caused by mitigation policies is comparable to SSP2 despite the much lower baseline poverty. At the same time, also the carbon pricing revenues are highest in SSP5, such that again the policy side effect can be compensated from the revenues. In SSP1, on the other hand, mitigation pressure is lower, and thus also the poverty increase caused by mitigation policies without redistribution is smaller. Again a full compensation of the poverty side effects is possible through progressive redistribution, leading to similar results across the three SSPs.

However, much of the heterogeneity between different regions and countries is lost when aggregating to these global figures, so that the total global headcount does not reflect potential hardships that are regionally concentrated. We therefore also discuss a regional breakdown of our results below.

Regional poverty trends

Countries of the Sub-Saharan African (SSA) region have the highest poverty rates today, and also in our projection for 2030 (Fig.  1 ). In addition, the increases in energy and particularly food expenditures triggered by carbon pricing are substantial (Supplementary Fig.  3 ). At the same time, the revenues from carbon pricing are modest, both due to the low per-capita emissions from the energy sector and the initially low carbon price. We thus find that climate policy without progressive redistribution policies would increase the poverty headcount in SSA substantially, by around 30 million in the SSP2 mitigation scenario without progressive redistribution (Fig.  4 ).

Most of this increase in poverty can be compensated through a progressive redistribution of the carbon pricing revenue, but even under this optimistic assumption there would be an increase in poverty by around 10 million people by 2030. Varying the socioeconomic baseline, we obtain similar trends as discussed above for the global headcount. Notably, however, in SSP1 a near-complete compensation of the policy side effect is also possible in SSA. This highlights that a generally more sustainable development pathway also reduces or avoids potential adverse side-effects of climate policies.

For India we project a fairly rapid reduction of poverty in the baseline scenario, in accordance with projections by the World Bank 38 . Against this background also the effects of climate policy are less severe (+7 million people in SSP2 by 2030 without progressive redistribution). In addition, also the carbon pricing revenues are higher than in SSA, and thus we project that poverty in India could even decrease if climate policy is implemented together with an equal-per-capita redistribution of the revenue (−10 million people by 2030). Interestingly, the poverty reduction achievable through progressive redistribution is lowest in SSP1, reflecting the already low baseline poverty and the (compared to SSP5) modest carbon pricing revenues.

In the other Asian countries (not including China, which is a separate model region) and in Latin America the trends are qualitatively comparable to India, but their contribution to the global numbers is smaller. Overall, our regional analysis reveals a strong heterogeneity in the ability of countries to compensate the distributional side effects of mitigation policies from their domestic carbon price revenue. While for most countries the revenues are sufficiently large to avoid an increase in poverty at least in the near term, this is not the case in Sub-Saharan Africa.

Options for generating poverty co-benefits

So far we have focused on the question whether an equal-per-capita redistribution of the carbon pricing revenues is sufficient to avoid poverty side effects of ambitious mitigation policies. We now investigate if it is possible to achieve a poverty co-benefit, i.e. a net reduction of poverty through a combination of climate policy and redistribution measures. One option to achieve this would be to redistribute the national revenues in a strongly progressive way to maximize their effect for poverty reduction. In addition, we explore two ways to increase the revenue base available for redistribution especially in developing countries, an inclusion of the revenues from pricing land-use related emissions, and an international climate finance mechanism funded from a fraction of the carbon pricing revenues.

Here we explore these three options for the SSP2 mitigation scenario, and show the results in Fig.  5 . Note that we also include results for 2050 into this assessment. In our ambitious mitigation scenarios, CO 2 -neutrality is achieved around this time. Therefore the small remaining carbon pricing revenues (Supplementary Fig.  3 ) in our default scenarios are insufficient to compensate the remaining policy side effects (around +30 million people in poverty in SSP2 after progressive redistribution).

figure 5

We show three different ways to enhance the poverty reduction co-benefits of the revenue recycling. The first option has the same revenue base as the default case (shown at the very left for comparison), but redistributes them in a strongly progressive way. The second and third options, land-use revenues and international transfers, increase the revenue base available for redistribution. Left and right panels are global results and Sub-Saharan Africa, respectively; top and bottom panels 2030 and 2050. Bars represent the central projection, error bars are 68% prediction intervals (see also caption of Fig.  4 ).

More progressive redistribution

We implement a strongly progressive redistribution scheme as a redistribution inversely proportional to income. We find that redistributing the carbon pricing revenues according to this scheme is able to fully compensate the poverty side effects of mitigation policies in SSA in 2030 (Fig.  5 ). As a consequence also the global poverty headcount is reduced to significantly below the baseline value (−50 million people). In 2050, on the other hand, even a strongly progressive redistribution cannot compensate the policy side effects, as the remaining carbon pricing revenues available for redistribution are small.

However, it is unclear whether such a scheme could be implemented in practice in most developing countries, how effective its targeting would be, and how much of the revenues would be absorbed by the cost of administering the scheme (see e.g. the discussion in Banerjee et al. 39 ).

Redistribution of revenues from land-use emissions

For our main results we have assumed that only carbon pricing revenues from the energy system are available for progressive redistribution policies, as pricing land-use emissions (in particular methane and nitrous oxide) likely comes with larger costs for implementation and monitoring. If, however, a pricing of these emissions from agriculture and other land uses could be achieved at modest transaction costs, it would increase the revenues available for redistribution policies (Supplementary Fig.  3 ). This is especially important for Sub-Saharan Africa, as a substantial part of the greenhouse gas (GHG) emissions in these countries originate from this sector. We find that the additional revenues—if redistributed progressively—are sufficient to compensate the increase in poverty in all countries of SSA, both in 2030 and 2050 (Fig.  5 ). As a result, we project global poverty figures to be reduced below the baseline value in 2030 (around 30 million people less), and to be nearly identical to the baseline results in 2050. While it is unclear if such a scheme could be implemented in the near term, it might be a useful measure for compensating residual side effects of mitigation policies as CO 2 neutrality is approached.

International climate finance

In our main analysis we have assumed that international burden sharing is implemented through a period of staged accession, where developing regions face substantially lower carbon prices than industrialized regions until 2050. In addition, we now implement an international climate finance mechanism in a stylized way by transferring 5% of the energy-sector carbon pricing revenues from the industrialized countries to the Sub-Saharan African countries, where they are redistributed alongside the domestic revenues. This implies international transfers of initially around 100 billion $/yr (around 0.2% of GDP of the donor countries), but decreasing towards mid-century as emissions are reduced. Note that this level of climate finance mirrors the commitment by industrialized countries during the UNFCCC negotiations and in the Paris Agreement 40 .

In combination with an equal-per capita redistribution scheme, such a mechanism would even lead to lower 2030 poverty rates than in the baseline scenario ( − 30 million people in SSA, − 45 million people globally). Already modest international transfers funded from a fraction of the carbon pricing revenues of industrialized countries are thus sufficient to overcome the residual trade-off between SDG 1 and SDG 13. Such a mechanism would also align with SDG 17 (‘partnership for the goals’, in particular target 17.2).

By 2050, on the other hand, carbon neutrality has largely been achieved in the industrialized countries, therefore we assume that also the climate finance transfers cease to exist. As a result, we again project an increase in poverty ( + 20 million people in SSA, + 30 million people globally). Therefore additional funds beyond the transfer of carbon pricing revenues would have to be sourced.

A concern related to international transfer mechanisms is that the financial inflows might have negative consequences for the economies of developing countries, potentially leading to a ‘climate finance curse’ 17 . However, our stylized scheme implies international transfers well below the ones suggested by common burden sharing schemes 14 , 41 . They are also well below the (largely unfulfilled) target of many developed countries to provide at least 0.7% of gross national income as official development assistance.

Sensitivity to mitigation target

It is well known that policy costs increase non-linearly with the stringency of the temperature target 42 , and especially so for low-income countries 41 . As such, also larger poverty side effects can be expected, but at the same time there are higher carbon pricing revenues available for redistribution policies. Repeating our analysis with less stringent mitigation targets corresponding to a well-below 2  ∘ C and a 2  ∘ C temperature target, we find that the net poverty outcome after progressive redistribution of the revenues worsens slightly for more lenient temperature targets. For the case of the 2  ∘ C temperature target, a small net increase in poverty remains (+5 million people globally in 2030; see Section  10 of SI and Supplementary Fig.  10 ). This reflects that ambitious targets lead to higher carbon pricing revenues in the near term, which, however, diminish more rapidly over time as CO 2 neutrality is approached faster.

Higher poverty line and longer-term prospects for poverty eradication

So far we have focused on the international poverty line of 1.90 $/day, and mostly on the 2030 horizon set by the SDGs. However, the international poverty line is also criticized as being too low for acceptable living standards in many countries (e.g. 43 , 44 , 45 ). We thus repeat our analysis with a higher poverty line of 5.50 $/day, which is motivated by the value currently used by the World Bank for upper-middle income countries. We focus on this higher poverty line when analysing the longer-term poverty trends until 2050.

We project that poverty figures as measured by this higher poverty line will remain high until mid-century, especially in Sub-Saharan Africa, but to a lesser extent also in India and certain countries of Latin America and Asia (Fig.  6 a). For the ‘middle-of-the-road’ SSP2 baseline we project a global poverty headcount of around 2.5 billion in 2030, and around 1.4 billion in 2050 (again without the additional effects of climate impacts). The other SSPs span a range between 280 million (SSP5) and 3.2 billion (SSP3) in 2050, with the latter being close to the current value. Again we find that at the global level SSP1 and SSP5, as well as SSP3 and SSP4, respectively, have broadly comparable poverty trends (Fig.  6 b).

figure 6

a National poverty rates in 2050 (SSP2). b Global poverty trends (all SSP baselines). See the caption of Fig.  1 b for a definition of central estimate and uncertainty bands. c Longer-term effects of mitigation policies on poverty eradication (multiple SSPs, 2050). See Fig.  5 for the color legend and definition of error bars of panels ( c and d ). d Effect of additional policies (SSP2, 2050).

Against this background of high baseline poverty rates, we also obtain substantial side effects of climate policy, which persist also after the redistribution of the small residual carbon pricing revenues (e.g. +200 million for SSP2 with equal-per-capita redistribution in 2050, Fig.  6 c). Again, this policy side effect is much less pronounced in SSP1, reinforcing our earlier finding that a more sustainable development pathway not only makes mitigation targets easier to achieve, but also reduces the side effects of climate policies. Out of the previously discussed additional measures, only a redistribution of revenues from pricing land-use emissions is able to largely compensate policy side effects on poverty (Fig.  6 d), demonstrating its value for longer-term poverty eradication.

Taken together, this analysis of longer-term trends highlights that eradicating poverty, and avoiding adverse side effects of climate policies, requires also looking beyond the 2030 horizon given by the SDGs. Substantially increased efforts towards poverty eradication are thus mandated, especially when considering a higher poverty line that goes beyond the 1.90 $/day definition of extreme poverty.

Poverty outcomes depend on the distribution of mitigation efforts between countries and over time, between sectors and income groups within countries, and on the use of the carbon pricing revenue. To our knowledge, our study is the first to capture all of these layers at least to some extent. Our main finding is that there are substantial side effects of mitigation policies on poverty eradication, but already a progressive redistribution of the national carbon pricing revenues is sufficient to largely compensate for them.

Nonetheless average per-capita income levels in developing countries would decrease considerably under ambitious mitigation policies (Supplementary Fig.  6 ), as even under our strongly differentiated carbon prices the international distribution of mitigation costs is regressive. Therefore there is still a need for an equitable international burden sharing. Indeed we find that already modest international climate finance transfers would lead to a substantial reduction in near-term poverty headcounts.

We aimed for a poverty analysis with global coverage until mid-century, which is only possible by taking a fairly aggregate perspective: we only use national statistics for the distribution of income, and do not distinguish between different final energy carriers or food commodities. A greater level of detail in the incidence of policy costs could be achieved with an input-output approach 12 , 33 , 46 . Sectoral poverty dynamics could be disentangled with a computable general equilibrium (CGE) setup connected to detailed household surveys 32 ; both of these techniques are however often limited to a static perspective.

Using the latter approach, Hussein et al. 32 find that ambitious mitigation policies in developing countries would increase poverty rates, similarly to our results without progressive redistribution. Campagnolo & Davide 34 also employed a CGE model, but use its results (e.g. public education expenditure, sectoral value added, unemployment) to drive regression models for inequality and poverty. While this models the effects of climate policy on poverty via these structural variables, it does not fully capture the direct distributional effects through energy and food prices and revenue recycling.

Our analysis has focused on these direct distributional effects but did not include other potentially heterogeneous effects of climate policies (e.g. employment loss/creation). Furthermore, we have not captured the increased income for agricultural households when food prices rise 20 , 47 , 48 . In our setting the main drivers for food price increases are emission pricing and land scarcity driven by land-based mitigation options. The former does not result in additional income for farmers, and it is unlikely that increased returns to land ownership would substantially benefit poor households 25 , 32 . Nonetheless, a more detailed coverage of potentially heterogeneous effects on the income side would be a valuable extension for future work. Should such a quantification become available, it can be incorporated into our framework by specification of the appropriate income elasticity.

Our progressive redistribution policies require sufficient institutional capacity, and we assume that there are no substantial transaction costs. The latter seems reasonable in the case of lump-sum transfers, but transaction costs could increase if regular small payments were made instead. Requirements for institutional capacity and costs for administering the policy would likely also increase for schemes that are more progressive than an equal-per-capita redistribution.

Instead of directly redistributing the carbon pricing revenues, governments could also use them to increase their spending for other poverty-reducing policies. This includes for example education spending, but also infrastructure development that is critical for achieving other SDGs, such as access to electricity, clean water, sanitation, transport and telecommunication 49 , 50 , 51 . While we do not attempt to directly quantify the effect of such policies, our different redistribution schemes can be seen as stylized explorations of different degrees of progressivity in spending the revenues from carbon pricing.

We reiterate that we did not include the effect of climate impacts, but focused on quantifying the poverty side effects of mitigation policies. At the same time, ambitious mitigation measures are of particular importance for the global poor, as they are most vulnerable to the impacts of climate change 6 , 8 , 10 . We further note that the effects of the COVID-19 pandemic on poverty eradication 52 , 53 are not captured in our projections, as the effects of the pandemic on mid- to long-term economic development are not yet clear.

Of course the effects of mitigation policies on developing countries are also broader than the one-dimensional metric of extreme poverty 17 , 54 , 55 . Including climate impacts, including the effects of the COVID-19 pandemic, and extending our approach to additional SDG dimensions are therefore high priorities for future research.

Such a quantitative and multi-dimensional assessment of SDG outcomes would substantially enhance the value of IAM scenarios in navigating trade-offs between different policy objectives. In particular, it would ensure that the policy recommendations emerging from studies of mitigation pathways avoid putting a disproportionate burden on the global poor. The latter is a necessary condition for jointly implementing the Paris Agreement and the SDG agenda.

Overview of methodology

We calculate poverty rates as a post-processing of a mitigation scenario computed with the IAM framework REMIND-MAgPIE 35 , 56 , 57 . The key steps of our analysis are evaluating the changes to the income distribution as a result of mitigation policies, and linking these changes to poverty outcomes. A flow chart summarizing the different analysis steps is provided in Supplementary Fig.  1 .

Here we first give a brief overview of our IAM framework and explain the scenario setup chosen for this work. Subsequently, we discuss how to compute the components required for our post-processing analysis from the IAM output, in particular the GDP loss, additional energy and food expenditures, and the carbon pricing revenue. In our newly developed distributional framework we distribute these to different income groups. This allows us to compute an income equivalent net of climate policy induced changes, as well as the corresponding Gini coefficients, in ambitious mitigation scenarios at the country level. We translate these into national poverty rates using a regression model calibrated on recent poverty and inequality data. Finally, we calculate national, regional and global poverty headcounts which form the main result of our analysis. Detailed explanations and derivations of many analysis steps are available in the SI.


REMIND-MAgPIE is an IAM framework coupling a global, multi-regional energy-economy-climate model (REMIND) to a spatially-explicit land-system model (MAgPIE). A description of the most salient features of the two individual models is given in the SI; they are also documented in detail in refs.  58 , 59 , 60 , 61 and references therein. The source code and documentation for both models are available online (link in SI).

The energy-economy and land-use systems are connected as follows: the carbon price from REMIND is applied to land-use-related emissions in MAgPIE, which are in turn also taken into account in REMIND. In addition, the availability of bioenergy as a mitigation technology links the two systems. The coupled system REMIND-MAgPIE is run in an iterative way: information about the CO 2 price, emissions and bioenergy demand and price are exchanged until a joint equilibrium is reached 35 , 56 , 57 .

Scenario description

Our assumptions for socioeconomic development follow the Shared Socioeconomic Pathways (SSPs 36 ). For the three SSPs implemented in REMIND-MAgPIE (SSP1, SSP2, SSP5), we compare a baseline scenario (without climate policy or climate impacts) to an ambitious mitigation scenario that limits the increase in global mean temperature to 1.5  ∘ C. Importantly, this comparison should not be misinterpreted as evaluating the trade-off between poverty in a world with unabated climate change, and poverty caused by mitigation measures. Instead, our baseline scenario forms a counterfactual reference case for socioeconomic development in the absence of climate change and mitigation measures (as do the SSPs in general). Our baseline therefore does not include the (very likely substantial) effects of climate impacts on poverty.

Our mitigation scenarios are implemented as a regionally differentiated carbon tax, which is adjusted endogenously such that a CO 2 budget of 900Gt from 2011 until the (endogenously determined) time of peak warming 62 , 63 is not exceeded. Climate policy starts after 2020 with a period of staged accession: in developed economies the CO 2 price increases steeply until the peak budget is reached, and flattens off afterwards. Developing countries, on the other hand, initially face a lower carbon price that converges to a globally uniform price by 2050 (Section  2 of SI). Our level of price differentiation goes significantly beyond what is assumed for the period of initial fragmentation in the Shared Policy Assumptions 64 , but is lower than what would be needed to equalize fractional mitigation costs 41 .

The carbon price levels required to meet the 1.5  ∘ C target are determined endogenously as part of the REMIND-MAgPIE optimization and are shown in Supplementary Fig.  2 . We find that for SSP2 the resulting carbon prices in 2030 would have to be around 330$ [USD 2005] in industrialized economies. On the other hand, Sub-Saharan-Africa would face a much lower (but still substantial) price of around 55$ in 2030. Carbon prices in our SSP1 mitigation scenario are similar to SSP2, reflecting a compensation between lower energy demands on the one hand, and a more restricted technology portfolio (e.g. limits on carbon capture and storage) on the other hand. Due to the high energy demand in SSP5, carbon prices would have to be around 50% higher than in SSP2.

While our IAM model runs use a time horizon until 2100, here we only discuss results until 2050, as in particular the projections for within-country inequality become increasingly uncertain for longer time horizons.

Policy cost metrics

Our distributional calculation is performed as a post-processing of the IAM runs. As a metric for the effect of climate policies on household incomes, we calculate the income equivalent net of climate policy induced changes. Importantly, this takes into account both the income and the expenditure side: when energy and food prices rise as a consequence of mitigation policies, individuals can purchase less of those goods with their income, making them poorer in real terms 18 , 32 , 65 . (Note that we also include price-induced changes in energy and food quantities; see Sec.  3.1 of SI for details). Equivalently, this can be viewed as evaluating the total welfare change from both the income and expenditure side using a monetary metric of utility 66 . We track the following components, which we calculate from the difference of mitigation and respective baseline scenario:

GDP loss: we take this as an aggregate measure of total income loss, as our IAM, unlike a CGE, does not provide a high sectoral resolution of the income side.

additional expenditures for final energy (FE)

additional expenditures for food

redistribution of the net GHG pricing revenue (see Section  3.2 of SI for details)

We express these components as a fraction of GDP in the baseline scenario, and denote them by δ GDP , δ FE , δ food and δ GHG respectively. A detailed description of how these components are computed is given in the SI; Supplementary Fig.  3 shows an overview of the resulting values. Note that we apply a rescaling to the prices for food commodities computed in MAgPIE to make them more representative of the prices that households in developing countries are confronted with (see Section  3.1 of SI).

In our main analysis we use the revenues from pricing CO 2 , CH 4 and N 2 O from fossil fuel use and industry emissions for direct redistribution policies. We also discuss how an inclusion of land-use related CH 4 and N 2 O emissions into the redistribution scheme would change poverty outcomes. The same holds true for an international transfer scheme that uses a fraction (5%) of the carbon pricing revenues from industrialized regions to offset policy side effects in developing countries (Section  3.2 of SI). We assume that these climate finance funds are transferred to the Sub-Saharan-African countries, and redistributed alongside the domestic revenues raised there. We focus the international transfer payments on this region, as our results show that most other countries are able to compensate the poverty side-effects from their domestic revenues.

As our distributional calculation is performed at the level of individual countries, we downscale the REMIND-MAgPIE results by assuming equal fractional costs (GDP loss, increased food and energy expenditures) and carbon pricing revenues for all countries belonging to the same model region. In other words, we assume that price increases, associated demand responses, macro-economic effects etc. are comparable for all countries within a region. This downscaling step is necessary for an analysis with global scope, as it is computationally not feasible to compute country-specific mitigation pathways while maintaining global coverage.

General distributional framework

In our distributional framework we start from a baseline income distribution and baseline price levels, and subsequently calculate the changes caused by mitigation policies using the four policy cost metrics δ j ( j  = {GDP, FE, food, GHG}) computed from the IAM output.

We assume that the average per-capita income in every country is given by the GDP/capita values for the respective SSP 67 ; the level of intra-national inequality is determined by the Gini projections by Rao et al. 68 . (Note that we harmonize the SSP Gini coefficients to the SSP2 values until 2020 to avoid divergence in the scenarios already in the historical period. The values we use for SSP{1,3,4,5} in our projections are thus shifted by the (mostly small) difference to SSP2 in 2020 compared to the original Gini coefficients by Rao et al.) Based on these inputs we model the baseline distribution of income in every country as y  ~ Lognormal( μ ,  σ ); see Section  4 of the SI for details.

The loss (or gain) due to policy cost category j for a person with baseline income y is then given by

this is derived from the initial lognormal distribution by requiring that losses are proportional to \({y}^{{\alpha }_{j}}\) while ensuring that the national average is preserved (see Sec.  4 of the SI for details). Here \(\bar{y}\) is the average per-capita income in the baseline scenario and α j is the income elasticity of mitigation costs for category j , which quantifies how the aggregate national costs are distributed. For example, α j  = 1 results in an equal relative income loss for all individuals, whereas α j  = 0 would imply the same absolute income loss and thus a highly regressive distribution of costs.

Eq. ( 1 ) forms the core of our distributional analysis. We now apply it to the different categories of changes to the income distribution calculated from the IAM output. A distinctive feature of this approach is that we model the changes in the entire income distribution, as opposed to existing approaches that only work with a small number of income groups (typically quintiles or deciles).

Distribution of policy costs and revenues

We decompose the total change in income equivalent (or welfare measured in monetary units) from climate policy into an income side and an expenditure side: average incomes are reduced, and households pay higher prices for food and energy (note that our final energy and food prices include the carbon price). At the same time, they can benefit from a redistribution of the revenues from carbon pricing. The income equivalent of an individual with baseline income y therefore changes to

note that in our sign convention all Δ j terms are positive. We calculate individual policy costs for each category as follows (see Section  5 of SI for details):

Overall GDP loss is assumed to be distributionally neutral ( α GDP  = 1).

Increased energy expenditures are distributed according to income-dependent final energy expenditure shares, reflecting the empirical finding that in low-income countries the energy expenditure share increases with growing income, whereas the opposite is true for higher-income countries 33 . We estimate the corresponding income elasticity of final energy expenditures, α FE , empirically from the data Dorband et al. 33 compiled from the World Bank’s Global Consumption Database 69 . This data set provides energy, food, and total expenditures at the level of four consumption groups per country for a large number of countries. The income elasticity relates to the final energy expenditure share computed from the survey data as

A detailed description of our empirical method is given in Section  5.2 of the SI.

For additional food expenditures we apply the same procedure as for energy. The resulting income elasticity of food expenditures, α food , reflects empirical findings that food expenditure shares decrease substantially with increasing per-capita income levels 70 .

Importantly, our distributional analysis also makes the effects of different redistribution schemes explicit. In contrast, standard IAM analyses with only one representative household per model region implicitly assume ‘perfect’ redistribution within every region, such that neither climate impacts nor policy costs change the level of inequality 71 . Here we implement three different schemes for the redistribution of the carbon pricing revenue:

The carbon pricing revenue is spent in a distributionally neutral way, i.e. changing the average income but not the level of inequality. Technically this is implemented as a redistribution proportional to income, i.e. Δ y GHG  =  δ GHG y . This approximates a case where the carbon pricing revenue is used to reduce other taxes, but not in a progressive way.

The revenue is used to fund a progressive redistribution, implemented as an equal per-capita payment for all individuals: \({{\Delta }}{y}^{{\rm{GHG}}}={\delta }^{{\rm{GHG}}}\bar{y}\) . This represents an optimistic scenario where all countries commit to redistribution policies alongside their climate policy. Note that this assumes functioning institutions, such that there are no substantial leakages or inefficiencies in the redistribution scheme.

To assess the potential co-benefits of strongly progressive redistribution policies funded from the carbon pricing revenue, we explore a scheme where redistribution is inversely proportional to income, i.e. α GHG  = −1. Note that such a scheme would have even larger requirements for institutional capacity than discussed for the equal-per-capita case above.

Monte Carlo simulation for new distribution

Subtracting additional expenditures and adding transfers from carbon pricing revenue changes the income distribution away from the initial lognormal case. We calculate the distribution in the climate policy scenario numerically with a Monte Carlo simulation, which models the population of a country in a given year with one million representative individuals. From these samples, we readily compute the average income and the Gini coefficient in the policy scenario for every country (see Section  6 of SI for details), which we use in the subsequent poverty analysis. We note, however, that also any other desired summary statistic (such as other inequality metrics, e.g. the Palma ratio 72 ) can be inferred from our method. As an intermediate result of our distributional analysis, we show and discuss average incomes and Gini coeffients for four representative countries in Section  7 of the SI.

Regression model for poverty outcomes

In the above we have computed changes to the income distribution through climate policy and the associated redistribution policies. We now connect these changes to poverty outcomes using a regression model with average income ( \(\bar{y}\) ) and Gini coefficient as main drivers. For our main analysis we define poverty following the international poverty line of 1.90$/day in 2011 PPP dollars, i.e. we assume a constant poverty line in real terms. In addition, we explore a higher poverty line of 5.50 $/day, especially for analysing longer-term poverty trends.

Denoting the share of the population in country c at time t that is above the poverty line by s c , t , our regression model is specified by

The logit transformation of the dependent variable s c , t maps the population share above the poverty line from the range 0–1 onto an unbounded range which can be conveniently fit with a linear model. (A broadly similar model, albeit without the logit transformation, was used by Campagnolo & Davide 34 .)

We compile a data set of 1160 country-year observations of poverty rates, Gini coefficients 73 , 74 and GDP/capita values 75 from 131 countries and use it to fit the model with the above specification. Our model provides an excellent fit to the data (adjusted R 2  = 0.93) and shows that as expected both average income and Gini coefficient are highly significant drivers for poverty outcomes (see Section  8 of the SI for details). Note that there are many other variables that are potentially important drivers for poverty, for example economic structure, education and institutional quality. Any time-invariant differences between countries are already captured by our country fixed effects. Some of the time-varying effects, e.g. education and a number of policy variables, are already included as drivers for the SSP Gini coefficients 68 , and as such they are also included indirectly in our poverty projections.

Note that we refrain from calculating the poverty shares directly from the cumulative distribution function of income for the following reasons. (i) It is likely that the tails of the distribution will deviate from the assumed lognormal form to a certain extent. As the bottom tail is particularly relevant for our analysis, this could lead to a bias. (ii) For obtaining reliable poverty estimates directly from the distribution, average incomes from household surveys would have to be used for fixing the mean of the distribution. As we are interested in future projections where household surveys are by construction unavailable, we index the mean of the distribution to GDP/capita, and work with GDP/capita projections from the SSPs.

Projecting poverty headcounts and uncertainties

We project national and global poverty headcounts in the baseline and mitigation scenarios and the respective differences as follows:

The regression model is used to calculate the baseline projections for the share of the population in a given country above the poverty line, s c , t from the average income and Gini scenario data.

National poverty headcounts are then given by

where N c , t is the population. Regional and global poverty figures are the appropriate sums of national headcounts.

Steps (1) and (2) are also repeated using the average income and Gini for the mitigation scenarios as calculated above, leading to the poverty projections for the mitigation scenario.

The difference between poverty figures in mitigation and baseline scenario can be attributed to the effects of climate policy.

The regression model also provides us with an estimate of the uncertainty in the relationship between average income and Gini coefficient and poverty outcomes. Based on this we compute 68% prediction intervals (including approximately one standard deviation around the central estimate for future projections given the regression model) for the national, regional and global poverty figures (see Section  9 of SI for details). We also calculate uncertainties for the differences between the respective policy and baseline scenario. Due to the correlation between policy and baseline results the uncertainty of the difference between policy and baseline is smaller than either individual uncertainty. This allows us to compute the additional poverty caused by mitigation policies with fairly high precision despite the larger uncertainty on the policy and baseline projections.

Reporting summary

Further information on research design is available in the  Nature Research Reporting Summary linked to this article.

Data availability

The IAM scenario data analysed in this paper are computed with models that are available open source (see SI for links to code repositories). The data on energy and food expenditures by income group (see Section  5.2 of SI for details) were kindly provided by the authors of Dorband et al. 33 , who in turn derived them from the World Bank’s Global Consumption Database 69 . The data shown in the main figures of this study are available in this repository: . Intermediate data sets generated or analysed in this study are available from the corresponding author on reasonable request.

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The authors are grateful to Ira Dorband, Lorenzo Montrone and Jan Steckel for sharing the data compiled in Dorband et al. 33 , and also for helpful support and valuable discussions concerning the further processing of these data. The authors also thank the members of the REMIND and MAgPIE groups for helpful discussions and/or technical support, in particular Lavinia Baumstark, Christoph Bertram, David Klein, Hermann Lotze-Campen, Franziska Piontek, Miodrag Stevanovic and Isabelle Weindl. This work has been funded through the projects SHAPE, CHIPS and NAVIGATE. SHAPE and CHIPS are part of AXIS, an ERA-NET initiated by JPI Climate. SHAPE is funded by FORMAS (SE), FFG/BMWFW (AT), DLR/BMBF (DE, Grant No. 01LS1907A), NWO (NL) and RCN (NO) with co-funding by the European Union (Grant No. 776608). CHIPS is funded by FORMAS (SE), DLR/BMBF (DE, Grant No. 01LS1904A), AEI (ES) and ANR (FR) with co-funding by the European Union (Grant No. 776608). NAVIGATE is funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 821124.

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There is growing awareness that actions by policymakers and international organizations to reduce poverty, and those to mitigate and adapt to climate change, are inextricably linked and interwoven. This paper examines relevant academic and policy literature and evidence on this relationship and explores the potential for a new form of development that simultaneously  mitigates  climate change, manages its  impacts , and improves the  wellbeing of people in poverty . First, as a key foundation, it outlines the backdrop in basic moral philosophy, noting that climate action and poverty reduction can be motivated both by a core principle based on the right to development and by the conventional consequentialism that is standard in economics. Second, it reviews assessments of the current and potential future impacts of weakly managed climate change on the wellbeing of those in poverty, paying attention to unequal effects, including by gender. Third, it examines arguments and literature on the economic impacts of climate action and policies and how those affect the wellbeing of people in poverty, highlighting the importance of market failures, technological change, systemic dynamics of transition, and distributional effects of mitigation and adaptation. Finally, the paper surveys the current state of knowledge and understanding of how climate action and poverty reduction can be integrated in policy design, indicating where further research can contribute to a transition that succeeds in both objectives.

Hans Peter Lankes, Rob Macquarie, Éléonore Soubeyran, Nicholas Stern, The Relationship between Climate Action and Poverty Reduction,  The World Bank Research Observer , Volume 39, Issue 1, February 2024, Pages 1–46,

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climate change and poverty research paper

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climate change and poverty research paper

Article contents

  • Introduction
  • Data and methods
  • Results and discussion
  • Limitations and recommendations for further research
  • Conclusions

Disaster risk, climate change, and poverty: assessing the global exposure of poor people to floods and droughts

Published online by Cambridge University Press:  02 March 2018

  • Supplementary materials

People living in poverty are particularly vulnerable to shocks, including those caused by natural disasters such as floods and droughts. This paper analyses household survey data and hydrological riverine flood and drought data for 52 countries to find out whether poor people are disproportionally exposed to floods and droughts, and how this exposure may change in a future climate. We find that poor people are often disproportionally exposed to droughts and floods, particularly in urban areas. This pattern does not change significantly under future climate scenarios, although the absolute number of people potentially exposed to floods or droughts can increase or decrease significantly, depending on the scenario and region. In particular, many countries in Africa show a disproportionally high exposure of poor people to floods and droughts. For these hotspots, implementing risk-sensitive land-use and development policies that protect poor people should be a priority.

1. Introduction

Globally, about 700 million people live below the US$1.90/day poverty line, with many more balancing just above it (World Bank, 2015 ). This substantial part of the world population is particularly vulnerable to external shocks, including those caused by natural disasters, such as floods and droughts. Such disasters can reduce household income and destroy houses and productive capital. For example, after the 2004 floods in Bangladesh, poor households affected by the flood lost more than twice as much of their total income as non-poor households (Brouwer et al. , Reference Brouwer, Akter, Brander and Haque 2007 ). This illustrates the consistent finding that poor people are more vulnerable to disaster events (Carter et al. , Reference Carter, Little, Mogues and Negatu 2007 ). By vulnerability, we refer to the fact that poor people are more susceptible to flooding, e.g. by the fact that they lose a larger fraction of their wealth when they are affected by a natural hazard or have a higher probability of suffering mortality (see e.g. Jongman et al. , Reference Jongman, Winsemius, Aerts, Coughlan de Perez, van Aalst, Kron and Ward 2015 ), and have more difficulty coping with it. They have a lower capacity to deal with shocks than non-poor households, due to lower access to savings, borrowing, or social protection (Kundzewicz and Kaczmarek, Reference Kundzewicz and Kaczmarek 2000 ; Masozera et al. , Reference Masozera, Bailey and Kerchner 2007 ; Highfield et al. , Reference Highfield, Peacock and Van Zandt 2014 ). By exposure we mean the location of people in flood-prone areas.

Natural disasters are a key factor for pushing vulnerable households into poverty and keeping households poor (Sen, Reference Sen 2003 ; Krishna, Reference Krishna 2006 ). Just as importantly, exposure to natural hazards may reduce incentives to invest and save, since the possibility of losing a home due to a flood, or livestock due to a drought, makes these investments less attractive (Elbers et al. , Reference Elbers, Gunning and Kinsey 2007 ; Cole et al. , Reference Cole, Gine, Tobacman, Topalova, Townsend and Vickery 2013 ). This vulnerability of poor people to natural disaster risk is particularly worrying in the context of climate change, which may change the frequency, intensity, and spatial distribution of floods and droughts (IPCC, Reference Field, Barros, Stocker, Qin, Dokken, Ebi, Mastrandrea, Mach, Plattner, Allen, Tignor and Midgley 2012 ). Therefore, future climate change may represent a significant obstacle to eradicating poverty (Hallegatte et al. , Reference Hallegatte, Bangalore, Bonzanigo, Fay, Kane, Narloch, Rozenberg, Treguer and Vogt-Schilb 2016 ).

Several previous studies have investigated statistical relationships between national-level economic indicators and reported disaster losses on a global scale to find out if poor countries are more affected by natural hazards (Kahn, Reference Kahn 2005 ; Toya and Skidmore, Reference Toya and Skidmore 2007 ; Ferreira et al. , Reference Ferreira, Hamilton and Vincent 2011 ; Shepherd et al. , Reference Shepherd, Mitchell, Lewis, Lenhardt, Jones, Scott and Muir-Wood 2013 ; Jongman et al. , Reference Jongman, Winsemius, Aerts, Coughlan de Perez, van Aalst, Kron and Ward 2015 ). Whilst these studies have found statistical relationships between experienced flood impacts and average income, they have not investigated the spatial or socioeconomic distribution of the losses within countries. Recent advances in the global spatial modelling of floods (Pappenberger et al. , Reference Pappenberger, Dutra, Wetterhall and Cloke 2012 ; Hirabayashi et al. , Reference Hirabayashi, Mahendran, Koirala, Konoshima, Yamazaki, Watanabe, Kim and Kanae 2013 ; Wada et al. , Reference Wada, van Beek, Wanders and Bierkens 2013 ; Winsemius et al. , Reference Winsemius, Van, Jongman, Ward and Bouwman 2013 , Reference Winsemius, Aerts, van Beek, Bierkens, Bouwman, Jongman, Kwadijk, Ligtvoet, Lucas, van Vuuren and Ward 2015a ) and droughts (Prudhomme et al. , Reference Prudhomme, Giuntoli, Robinson, Clark, Arnell, Dankers, Fekete, Franssen, Gerten, Gosling, Hagemann, Hannah, Kim, Masaki, Satoh, Stacke, Wada and Wisser 2014 ; Schewe et al. , Reference Schewe, Heinke, Gerten, Haddeland, Arnell, Clark, Dankers, Eisner, Fekete, Colón-González, Gosling, Kim, Liu, Masaki, Portmann, Satoh, Stacke, Tang, Wada, Wisser, Albrecht, Frieler, Piontek, Warszawski and Kabat 2014 ) have led to improved estimates of the global population exposed to natural hazards, but these assessments have not addressed different income groups.

To our knowledge, the relationship between poverty and exposure to floods and droughts has only been studied on a case-study basis for a few countries. A literature review of 13 of such studies, conducted in this paper, shows that poor people are often disproportionately overrepresented in hazard-prone areas. As shown in figure A2 (online appendix), only one of the 13 studies finds that non-poor people are more exposed than poor people. Although these cases highlight a possible relationship between poverty and exposure, evidence on the global representativeness of these case-study results and general figures on the exposure of poor people is lacking.

In this paper, we analyse global exposure of poor and non-poor people to river floods and droughts under current and future climates. To do this, we combine hazard maps from global river flood and hydrological drought models with detailed household wealth and income datasets for 52 countries. At this stage, we have not yet included coastal flooding, which would result in additional flood impacts. Poverty is defined here using the distribution of wealth amongst households within a given country. We explore whether there is a significant exposure bias for either poor or non-poor people to river floods and droughts and whether their exposure will increase in the future. As data limitations create certain constraints on the analysis, this study should be treated as a first-cut exploration.

In this section, we review the complex relationship between poverty and exposure to natural hazards. The relationship between poverty and exposure may go in both directions. First, poor people may be more likely to settle in flood- and drought-prone areas. Second, households affected by floods and droughts have a higher risk of falling into poverty or being trapped in poverty. Both aspects are discussed below.

Localization choices across regions and cities are driven in the first place by socioeconomic considerations (housing prices, proximity to jobs, amenities) much more than by natural hazards (Hallegatte, Reference Hallegatte 2012 ). Households may be willing to accept high levels of risk to get access to opportunities. For example, in Mumbai, households in flood areas report that they are aware of the flood risks, but accept them due to the opportunities offered by the area, such as access to jobs, schools and health care facilities (Patankar, Reference Patankar 2016 ). Compounding this incentive for people to reside in flood zones and close to opportunities is the reality that transport is often unreliable, unsafe, or expensive (Dudwick et al. , Reference Dudwick, Hull, Katayama, Shilpi and Simler 2011 ; Gentilini, Reference Gentilini 2015 ). In some rural areas, proximity to water offers cheaper transport opportunities and regular floods may increase agricultural productivity (Loayza et al. , Reference Loayza, Olaberría, Rigolini and Christiaensen 2012 ). People may also settle in risky areas to benefit from opportunities with industries driven by exports in coastal areas (Fleisher and Chen, Reference Fleisher and Chen 1997 ). These opportunities attract all people –rich and poor –to places that are exposed to natural hazards.

However, at the city or neighbourhood level, where the opportunity factors are broadly similar but risk of floods may be different from neighbourhood to neighbourhood, poor people might be more exposed due to lower housing prices in flood zones (Bin and Landry, Reference Bin and Landry 2013 ). A meta-analysis of 37 empirical studies, mostly in developed countries, found that prices between flood-exposed and non-flood-exposed houses varies widely, ranging between −7 per cent to +1 per cent (Beltran et al. , Reference Beltran, Maddison and Elliott 2015 ). Poorer people, with fewer financial resources to spend on housing and a lower willingness and ability to pay for safety, are more likely to live in at-risk areas. This factor is more likely to exist for floods than for droughts, due to the small-scale variability in flood hazard. For example, with floods, impacts can be very different in areas 100 meters apart.

Alternatively, causality may go from flood and drought exposure to poverty. Evidence shows that floods affect household livelihood and prospects, and increase local poverty levels, through the loss of income and assets (see e.g. Rodriguez-Oreggia et al. , Reference Rodriguez-Oreggia, De La Fuente, De La Torre and Moreno 2013 for an analysis in Mexico). Exposure to droughts has been found to increase poverty ex-post (Dercon, Reference Dercon 2004 ; Carter et al. , Reference Carter, Little, Mogues and Negatu 2007 ). Furthermore, the impact of disaster risk on poverty occurs through both the visible ex-post channel (the losses when a disaster occurs), as well as the less obvious ex-ante channel: households exposed to weather risk have been shown to reduce investment in productive assets and to select low-risk, low-return activities (Elbers et al. , Reference Elbers, Gunning and Kinsey 2007 ; Cole et al. , Reference Cole, Gine, Tobacman, Topalova, Townsend and Vickery 2013 ). This link from natural hazard exposure to poverty may create a feedback loop, in which poor households have no choice but to settle in at-risk zones and therefore face increased challenges to escaping poverty.

3. Data and methods

We examine relationships between poverty and exposure to river floods and hydrological droughts by combining flood and drought hazard maps from a global hydrological model with household level poverty data for 52 countries. River floods are identified from larger rivers (in the order of 10,000 km 2 upstream area and above) only, and hydrological droughts are defined as climatological anomalies in river flows. The household data are taken from household surveys from the Demographic and Health Surveys (DHS), which are carried out by ICF International and hosted by the United States Agency for International Development (USAID).

In brief, per country we first analyse the wealth of households in all areas, and then the wealth of households in areas prone to river floods and/or hydrological droughts, and examine the difference between them. We do this by checking for each individual household whether its geographical position is within a flood/drought prone area or not. Using a precise geographical location is important in particular for floods, as floods can be a very local phenomenon. In the following subsections we describe the data and methods used. More detailed information about data and methods can be found in a background paper by Winsemius et al. ( Reference Winsemius, Jongman, Veldkamp, Hallegatte, Bangalore and Ward 2015b ). The overall workflow is shown in figure 1 , for the example of Colombia.

climate change and poverty research paper

Figure 1. Flow-chart visualizing the modelling and analysis procedure for Colombia. The hazard maps show the distribution of flood and drought events as simulated using the global hydrological model PCR-GLOBWB under the EU-WATCH (1960–1999) scenario, with a return period of 100 years.

3.1 Deriving the flood and drought indicators

We use a global hydrological model, PCRGLOB-WB (Winsemius et al. , Reference Winsemius, Van, Jongman, Ward and Bouwman 2013 ) run with the EU-WATCH Forcing Data (Weedon et al. , Reference Weedon, Gomes, Viterbo, Shuttleworth, Blyth, Österle, Adam, Bellouin, Boucher and Best 2011 ) to derive maps showing indicators of flood and drought hazard. PCRGLOB-WB in brief estimates globally, at 0.5×0.5 degree resolution (about 50×50 km at the equator) on a daily basis over a given run time, how much rainfall runs off to rivers, and how this runoff accumulates in the river network and travels downstream. We use the WATCH Forcing Data, providing 0.5 degree gridded meteorological data needed to drive the model (precipitation, temperature and potential evaporation), to run this model over a 40-year period (1960–1999). From the resulting discharge and water depth time series at 0.5 degree resolution, we derive the hazard indicators for floods and droughts for several return periods (i.e., one divided by the average exceedance probability per year of a flood or drought event of a given magnitude, further described below). An event with associated return period should be interpreted as follows: an event with a very high return period (i.e., an event happening very infrequently) is more severe than an event with a low return period (i.e., a more frequently occurring event). Below we provide a brief description of the model cascade and derivation of flood and drought maps. We provide a more elaborate description in online appendix A. For simplicity, we focus on results for 10 and 100-year return periods.

3.1.1 Flood hazard

Flood hazard is represented by flood inundation depth maps at 30″ (arc seconds) ×30″ resolution (approx. 1 km × 1 km at the equator) from the GLOFRIS model cascade, which uses PCRGLOB-WB for its hydrological boundary conditions. In short, the water depths, associated with a given return period (see section 3.1 ) at 0.5 degree resolution, are downscaled to a much finer resolution using a much more granular elevation dataset. To define whether there is a flood hazard, we applied a threshold set at 0 m (i.e., any flooding occurring is hazardous). GLOFRIS is described in detail in Winsemius et al. ( Reference Winsemius, Van, Jongman, Ward and Bouwman 2013 ) and applied at the global scale in several studies (Ward et al. , Reference Ward, Jongman, Weiland, Bouwman, van Beek, Bierkens, Ligtvoet and Winsemius 2013 ; Jongman et al. , Reference Jongman, Winsemius, Aerts, Coughlan de Perez, van Aalst, Kron and Ward 2015 ; Winsemius et al. , Reference Winsemius, Aerts, van Beek, Bierkens, Bouwman, Jongman, Kwadijk, Ligtvoet, Lucas, van Vuuren and Ward 2015a ). The method does not consider flood protection, as this is relatively low in developing countries. It also does not include coastal floods and flash floods. More details on the derivation of flood hazard maps from the runs with PCRGLOB-WB are provided in online appendix A2.

3.1.2 Drought hazard

We applied a variable monthly threshold method (namely the 80 per cent exceedance probability of discharge, Q 80 ) to estimate the yearly maximum cumulative discharge deficit, that is, the accumulated amount of discharge under the Q 80 threshold over a continuous period of time, per grid cell at 0.5 ° resolution as a measure of hydrological drought (Lehner and Döll, Reference Lehner, Döll, Lehner, Henrichs, Döll and Alcamo 2001 ; Wada et al. , Reference Wada, van Beek, Wanders and Bierkens 2013 ; Wanders and Wada, Reference Wanders and Wada 2015 ), using outputs from PCR-GLOBWB. Figure A3 in the online appendix shows the definition of droughts in a graphical form.

The resulting maps express the intensity of droughts relative to long-term mean discharge and can be interpreted as the amount of time a long-term mean discharge would be needed to overcome the maximum accumulated deficit volume occurring with a certain return period. We assumed that hazardous conditions occur when this value exceeds 3 months, and tested the robustness of our results using 1-month and 6-month thresholds. The indicator does not include information on groundwater availability or upstream water use. The resulting drought values should therefore be interpreted as conservative (underestimating drought hazard). Naturally, much more sophisticated drought indicators can be derived by accounting for season, rain-fed or irrigation based agriculture, locally specific demands, but these would all require much more local information and cannot easily be used at the global level.

3.1.3 Future flood and drought hazard

The model was also used to estimate future climate change impacts on flood and drought hazard, for different time periods (1960–1999, 2010–2049, 2030–2069, and 2060–2099), using meteorological outputs from five Global Climate Models (GCMs), forced by two representative concentration pathways (RCPs) (Van Vuuren et al. , Reference Van Vuuren, Edmonds, Kainuma, Riahi, Thomson, Hibbard, Hurtt, Kram, Krey, Lamarque, Masui, Meinshausen, Nakicenovic, Smith and Rose 2011 ), which represent scenarios of future concentrations of greenhouse gases (RCP 2.6 and 8.5, consistent with a 2 and 4 ° C increase, respectively). By ‘forced,’ we mean that the GCM outputs are generated by running the GCMs with the concentration of greenhouse gases in the atmosphere prescribed in the RCP scenarios. We have used RCP 2.6 and 8.5 so that we show two very contrasted developments in climate change. Note that climate change does not make floods and drought risks become more severe everywhere. In some regions, floods become less severe and frequent due to reduction in rainfall (shown e.g. by Hirabayashi et al. , Reference Hirabayashi, Mahendran, Koirala, Konoshima, Yamazaki, Watanabe, Kim and Kanae 2013 ; Winsemius et al. , Reference Winsemius, Aerts, van Beek, Bierkens, Bouwman, Jongman, Kwadijk, Ligtvoet, Lucas, van Vuuren and Ward 2015a ); in others, increase in precipitation reduces drought severity. Since the GCMs used contain bias due to unrepresented intra-annual and inter-annual variability, we use the difference in annual exposed people between GCM-forced model runs in the future and the past to establish changes in exposure.

3.2 Poverty data sets

A comprehensive spatial database to examine the distribution of poverty within and across countries is not yet available at the required spatial resolution. Footnote 1 However, household surveys contain some spatial information to approximate the location of a household, which we employ in this analysis. Our main analysis is undertaken using the ‘wealth index’ (e.g. Barros et al. , Reference Barros, Ronsmans, Axelson, Loaiza, Bertoldi, França, Bryce, Boerma and Victora 2012 ; Fox, Reference Fox 2012 ; Ward and Kaczan, Reference Ward and Kaczan 2014 ) from the USAID's DHS surveys. This index is available across 52 countries that contain geo-referenced household-level data. These countries represent about 23 per cent of the world's population. There are typically 500–1,000 survey clusters for each survey, with each cluster containing approximately 25 households.

All households in each country are classified in five quintiles (with quintile 1 having the lowest wealth, and quintile 5 the highest). We furthermore classified urban and rural households into quintiles, which enabled us to investigate the exposure across urban and rural populations separately.

3.3 Analysing the relationships between poverty and floods/droughts

To investigate the global exposure of poor people to floods and droughts, we define a ‘poverty exposure bias’ (PEB) that measures the fraction of poor people exposed, compared to the fraction of all people exposed per country. When estimating the number of people exposed, we multiply the exposed households by their household size and use household weights to ensure the representativeness of our results at the national level. The household weight is a measure of the representativeness of the household related to all other households. We compute the PEB using:

where I p is the PEB, f p and f are the fraction of people exposed to floods/droughts in the country (estimated by individually overlaying household location with our flood/drought maps, see section 3 ) in the poorest household quintile within a country and in the entire population, respectively. If I p is lower than zero, poor people are less exposed to floods/droughts than average. If I p is above zero, poor people are more exposed than average. Since the wealth index is comparable only within and not between countries, the PEB quantifies whether poor people are more or less exposed compared to the entire population within a specific country. Aggregation of all wealth index data for all countries and computation of a single global PEB is not possible with the data currently available. We tested our method for robustness regarding uncertainty in the geographical location and sample size using the methods described in online appendix A4, and robustness estimates are used in the description of our results.

4. Results and discussion

All results are summarized in table 1 . Below we describe and analyse the distribution of the results for floods and droughts.

Table 1. Poverty exposure bias and increase in exposure for floods and droughts

Notes : For countries where none of the households within the DHS survey were exposed, not available (NA) is stated. Significant results appear in bold type.

4.1 Geographic distribution of the PEB under present-day climate

4.1.1 floods.

Figure 2 shows the PEB for floods with a return period of 10 years. The results for a higher return period of 100 years exhibit very similar patterns (not shown here). For floods at the national-level, under present-day climate conditions, 34 out of the 52 countries show a significant result when testing the exposure bias by means of bootstrapping. Of these 34, half (17) exhibit a disproportionally high exposure of poor people to floods. This result supports the general notion that the relationship between poverty and disaster exposure is impacted by multiple channels and is therefore complex. For instance, where non-poor are more affected by floods, this could mean that the regions investigated offer amenities to richer households, or that the areas are equipped with flood protection to facilitate households. Using country-level population data (World Bank, 2015 ), we find that these 17 countries include 60 per cent of the analysed population.

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Figure 2. PEB for 10-year return period floods. White areas are not part of the 52 country sample. Areas are dotted when there is a lower than 95% confidence that the sign of the exposure bias is as estimated.

Moreover, regional patterns become visible. In particular, countries in Southern Africa, the Horn of Africa (except Ethiopia, Rwanda, Zimbabwe and Mozambique), and Egypt have a disproportionally high exposure of poor people to floods, although not all countries show significant results (Tanzania and Democratic Republic of Congo). In Western Africa, the results are mixed, although in countries with larger rivers and delta areas (notably Benin, Nigeria and Cameroon) there appears to be a tendency towards poor people being disproportionally exposed to floods. In Asia, poor people are disproportionally exposed (by a moderate but significant amount) in Indonesia; the same can be seen for Central and South America in Colombia and Guyana.

There are also several countries where poor people are less exposed to floods than average. These include some of the Asian countries in our sample (Cambodia, Nepal and Philippines, although the PEB for the last is insignificant), some West African countries, and most of the countries investigated in Central and South America.

The same analysis was performed using a quintile subdivision over only rural and urban households (that is, examining the PEB only within urban areas and only within rural areas). The results for urban households demonstrate a clear difference: in most countries poor urban households are clearly more exposed to floods than the average urban population ( figure 3 ). Of the 30 countries with significant results, 22 exhibit a positive exposure bias (73 per cent in population terms). This suggests that the national poverty exposure bias may be largely driven by the wealth differences and hazard exposure differences between rural and urban households. There is no such strong signal for rural households, suggesting that different mechanisms may be at play in rural and urban settings. For instance, land scarcity may be more acute in urban areas (than in rural areas), creating a stronger incentive for poor people to settle in risky areas due to lower prices. We have also tested how spatially variable the overrepresentation of poor people can be, by performing an additional assessment on a much more local scale for Morocco and Malawi (see online appendix B). This suggests that very local differences in exposure may be experienced as well.

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Figure 3. PEB for 10-year return period floods, for urban households only. Note that the quintile subdivision used is based on urban households only. White areas are not part of the 52 country sample. Areas are dotted when there is a lower than 95% confidence that the sign of the exposure bias is as estimated.

4.1.2 Droughts

Figure 4 shows country level PEB for droughts with a return period of 100 years. Again, the results for other return periods are similar, although the very low return period results yielded no exposed households in many countries. Of 30 countries with significant results, 24 exhibit a disproportionally high exposure of poor people (85 per cent in population terms). In all countries studied in Asia and in many countries in Southern and Western Africa, we find a clear signal that poor households are more exposed to droughts than average. For instance, Ghana, Togo, Benin, Nigeria and Cameroon in a row all show a signal of higher exposure to droughts of poor households compared to average. Other countries to the north and west show the opposite result, i.e., more exposure to droughts for non-poor households. In Central and South America, poor people appear less exposed in Bolivia and Peru, but more exposed in Colombia, Guyana and Honduras.

climate change and poverty research paper

Figure 4. PEB for 100 year return period droughts. White areas are not part of the 52 country sample or have no exposure to droughts at all.

Many Sub-Saharan African countries show a positive PEB for droughts as well as floods. In many parts of Africa, many poor people are subsistence farmers, and therefore very dependent on reliable rainy seasons, which makes them more vulnerable to drought. A similar analysis for rural and urban households does not reveal significant differences with the country-scale analysis (see figures A4 and A5, online appendix). This may be due to the different scales of flood and drought hazards. Our flood indicator (and flood processes in general) has a higher spatial resolution (and variability) than drought.

4.2 The impact of climate change

Climate change is likely to increase the number of people exposed to floods and droughts. To estimate the range of increase in population exposure, we overlay future projected flood and drought hazard maps with present-day population density data. Footnote 2 We use a high-emissions pathway consistent with a 4 ° C increase in global temperatures, the Representative Concentration Pathway (RCP) 8.5. We run the analysis for five GCMs. Footnote 3 Across the GCMs, for droughts we find that the number of people exposed could increase by 9–17 per cent in 2030 and 50–90 per cent in 2080. For floods, the number of people exposed to floods could increase by 4–15 per cent in 2030 and 12–29 per cent in 2080.

To assess how poverty exposure may change in the future due to climate change, we calculate PEB for a low-emissions pathway RCP 2.6 (consistent with a 2 ° C increase) and high-emissions pathway RCP 8.5 (consistent with a 4 ° C increase) and for five GCMs. To ensure that we only see the impact of climate on exposure, we do not include compounding effects such as migration and population growth.

The PEB does not change significantly under the two different future climate scenarios and is therefore not displayed. Of course, hazard does not drive exposure and exposure bias alone. The expectation is that PEB will change in the future due to other driving mechanisms not assessed in this paper, such as migration, changes in the spatial distribution of poverty, or the general increase in income within countries. Countries with rapid urbanization may exhibit major changes in flood exposure patterns in the coming decades, independently of climate change and other changes in hazards.

Regions where climate change causes an increase in the annual expected number of people exposed to floods and droughts, and where poor people are already more exposed than average (i.e. I p  > 0), should be treated as highly climate-sensitive regions for poor people. To locate these, figure 5 shows the percentage change per country in the annual expected number of people exposed to floods between 1980 and 2050, based on the household data and RCP 8.5, and figure 6 shows the same for droughts ( table 1 also reproduces results for all countries). RCP 2.6 shows similar changes in exposure, although it takes longer before these changes are reached. In some countries, the number of flood-exposed people under climate change rises rapidly; this is the case in the Horn of Africa, parts of West Africa, Egypt, Bangladesh, Colombia, and Bolivia. For droughts, the different GCMs show more disagreement in drought extremes, causing less significant results. However, if we use the GCM ensemble mean we see that West African countries in particular show an increase in the number of exposed people.

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Figure 5. Percentage change in nationwide average annual number of flood-exposed people in our sample of 52 countries following RCP 8.5 from 1980 until 2050. The GCM ensemble average is shown. Countries where the GCM ensemble standard deviation is higher than 50% of the GCM mean are dotted.

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Figure 6. Percentage change in nationwide average annual number of drought-exposed people in our sample of 52 countries following RCP 8.5 from 1980 until 2050. The GCM ensemble average is shown. Countries where the GCM ensemble standard deviation is higher than 50% of the GCM mean are dotted.

Finally, we have determined countries where a combination of disproportionally exposed poor and exposure increase is observed. We have determined these as countries with a PEB larger than 10 per cent (i.e., poor people are disproportionally exposed) and an increase in the amount of total exposed people larger than 10 per cent. Under RCP 8.5, in 2050, the marked countries include Egypt, Guinea, Kenya, Nigeria, Sierra Leone, Uganda and Bangladesh. For droughts, only Nigeria, Ghana and Togo are facing this situation. Footnote 4 These are predominantly African countries, located above the equator. Here, climate change-induced flooding will likely hit poor people the hardest, although less than half of the countries have both an overexposure of poor people to floods and an expected increase in flood risks due to climate change.

5. Limitations and recommendations for further research

We found a high variability in results between countries; poor people are not over-exposed to natural hazards everywhere. However, the analysis is limited by data availability, as the DHS samples are too small to look at regions and within-country variability. The limited number of households per country has implications for the results for droughts in particular: in many countries, there is no overlap between zones with extreme drought conditions (e.g. a minimum of 3 months drought, at 100 year return period, yielded only 15 countries with significant results) and households, meaning that no estimate of the PEB for droughts could be made in these cases. A larger number of observations per country would therefore make the results of our analysis more robust.

A related limitation is the spatial scale of the analysis. DHS samples are rarely representative within sub-national regions, which limits our ability to examine the poverty exposure bias within specific regions of a country. Higher-resolution data (e.g. poverty maps within a city) would be able to better capture dynamics at the local level, where lower land prices may push poorer people into more risky areas. Furthermore, the DHS data are clustered with between 500–1000 clusters per country. This modest number of clusters means that some areas that are flood or drought prone may not be covered by the DHS data, limiting our ability to test robustness.

Ideally, we would compare our results across countries and not just within them. However, the wealth index calculated by DHS is country-specific, meaning that the same value for the wealth index across two different countries may imply a different level of wealth. While some authors have recently suggested that the DHS wealth index may be compared across countries (Rutstein and Staveteig, Reference Rutstein and Staveteig 2014 ), country-to-country comparability remains difficult. This is one reason why we use relative thresholds (e.g. quintiles) rather than absolute ones. Another reason for the use of relative numbers is that, in case of an absolute poverty threshold in some countries, an overwhelming majority of the population would be classified as poor, hampering the envisaged analysis.

In this study, we have not investigated factors that influence the vulnerability of households to flooding, such as the building quality, or other determinants of flood impacts such as flood duration (Parker et al. , Reference Parker, Green and Thompson 1987 ; Dang et al. , Reference Dang, Babel and Luong 2010 ), and its impact on indirect losses such as loss in output and revenue and economic disruption (Lekuthai and Vongvisessomjai, Reference Lekuthai and Vongvisessomjai 2001 ) and flood-related health issues; and flood level rise rate which is especially important in terms of mortality (Jonkman et al. , Reference Jonkman, Maaskant, Boyd and Levitan 2009 ). More research is required to examine how these could impact on poverty (for a review, see Hallegatte et al. , Reference Hallegatte, Vogt-Schilb, Bangalore and Rozenberg 2017 ).

Similarly, households that are highly vulnerable to droughts (e.g. with assets strongly relying on water) may experience problems even during a one-month drought condition, although others may only experience problems if the drought lasts three months or more. To assess the robustness of the drought indicator applied, we also tested our results using a one-month and six-month threshold (shown in Winsemius et al. , Reference Winsemius, Jongman, Veldkamp, Hallegatte, Bangalore and Ward 2015b ). More people are exposed with a one-month threshold than with a six-month threshold. For the aggregated PEB results, we could only find a significant number of exposed households in six countries using a six-month drought threshold with a return period of 10 years. This increases to up to 50 countries when considering one-month droughts as a threshold with a 100 year return period. Notably, median PEB values are above zero for the 100 year return-period drought, and decrease toward and below zero for lower return-period (10 years) droughts and higher drought thresholds.

This suggests that the small sample sizes make it difficult to find a robust exposure bias pattern in many countries. Nonetheless, we found consistent results on the sign of the PEB for sub-Saharan Africa (Nigeria, Cameroon, Democratic Republic of Congo, Togo, and Benin (not significant for a one-month threshold)), Southeast Asia (Philippines, Indonesia (not significant for a six-month threshold)) and Colombia (when comparing the one, three and six-month threshold results under the 100-year return period). Other countries showed mixed results over the different threshold values and therefore results over these countries should be treated with lower confidence.

6. Conclusions

The general conclusion of this study is that, in a large number of the countries investigated, poor people are disproportionally exposed to droughts and urban floods. But the situation differs strongly between countries, within countries, and based on the type of hazard. However, there are geographical patterns: the countries where the strongest bias in exposure of poor is found are concentrated in Africa for both perils. Thirteen out of 23 countries in Africa with significant results show a positive PEB, most of which are found in the region under 10 ° N latitude. For droughts, we found significant results in only 30 out of 52 countries, due to the low amount of sample observations for our estimate of PEB. Nonetheless, of these 30 countries, 24 (representing 85 per cent of the population within the countries with significant results) show a positive PEB to droughts.

We find that in urban areas, poor people are disproportionally exposed to floods compared to average, while such a signal is not found for rural households. This is particularly noticeable in Africa, with the exception of several western African countries. In some countries, the absence of disproportionate exposure of poor at the national level may be due to the large gap in wealth between cities and rural areas, combined with the fact that flood hazard is often high in cities. The urban-rural gaps in income and flood risk may thus hide the fact that poor people are more exposed.

A particular concern is the fact that some of the countries where poor people are overexposed will also experience more frequent flooding or droughts in the future due to climate change. We see this in Burkina Faso, Burundi, Egypt, Ethiopia, Guinea, Kenya, Nigeria, Sierra Leone and Uganda for floods. For drought, Nigeria and Ghana were found to be in this situation, although results for Ghana were found to be less robust.

Exposure, the topic of this paper, is only one component of risk. Almost everywhere, the other risk components—from protection to vulnerability to the ability to cope and recover—are also biased against poor people (Hallegatte et al. , Reference Hallegatte, Bangalore, Bonzanigo, Fay, Kane, Narloch, Rozenberg, Treguer and Vogt-Schilb 2016 ), which means that even in places without a poverty bias, poor people may still experience higher risk. Protection levels and quality are lower in poor countries and lower in poor neighbourhoods and regions. Poor people live in low quality houses that suffer more damage in case of floods, and they have most if not all of their assets in material form, making them more vulnerable to floods. Finally, poor people have limited access to recovery support, such as social protection and credit.

A recent report (Hallegatte et al. , Reference Hallegatte, Vogt-Schilb, Bangalore and Rozenberg 2017 ) assessing the well-being impacts from natural disasters suggests that when including all these dimensions—exposure, vulnerability, and the ability-to-adapt—the impact of extreme weather on poverty is more devastating than previously understood, responsible for annual consumption losses of US$520 billion and for pushing 26 million people into poverty every year. The results from this paper on the distribution of the poverty exposure bias across countries were used as an input to the report's analysis, and are one example of an application of this paper's findings.

Disaster risk management and poverty reduction go hand in hand. In countries where poor people are disproportionally exposed to floods and droughts, it is particularly important to integrate risk management policies within poverty reduction strategies, to understand the underlying drivers of the exposure bias, and to correct it through better land-use regulation and other supporting policies. Critically, such policies should support the access of poor people to opportunities, and not stifle them. In locations where hazards will become more frequent or more intense, implementing risk-sensitive land-use policies that protect poor people, such as flood zoning and land entitlement, should be a priority.

Supplementary material

The supplementary material for this article can be found at .


We thank Adrien Vogt-Schilb and Anne Zimmer for their careful review of this paper, and Tom Pullum, Ruilin Ren, and Clara Burgert from ICF International for their very helpful guidance on using the DHS data. We are grateful for financial support from the Global Facility for Disaster Reduction and Recovery (and thank Alanna Simpson as being the main counterpart from GFDRR) and the World Bank, under the work program on ‘Poverty and Climate Change,’ led by the Office of the Chief Economist of the Climate Change Group. We also acknowledge support from Earth2Observe, EU FP7 project grant agreement no. 603608. Furthermore, P.J. Ward received additional financial support from a VIDI grant for the Netherlands Organisation for Scientific Research (NWO) (grant number 016.161.324).

1 Note that recent initiatives try and estimate global poverty at high-resolution gridded scales, see for example WorldPop at (accessed 14-12-2017).

2 For present day population, Landscan is used ( )

3 A factor delta approach was used to bias correct for the GCM uncertainty. That is, we examined the factor increase between historical GCM runs and future ones (2030,2080) and superimposed this factor increase on top of the EUWATCH results.

4 Although the low CO 2 concentration scenario (RCP 2.6) shows similar patterns (not shown here), the increase in floods/droughts for 2050 is lower and also the number and share of people exposed does not rise as fast as in the high concentration scenario (RCP 8.5).

Figure 0

Winsemius et al. supplementary material 1

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A review of the global climate change impacts, adaptation, and sustainable mitigation measures

Kashif abbass.

1 School of Economics and Management, Nanjing University of Science and Technology, Nanjing, 210094 People’s Republic of China

Muhammad Zeeshan Qasim

2 Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing, 210094 People’s Republic of China

Huaming Song

Muntasir murshed.

3 School of Business and Economics, North South University, Dhaka, 1229 Bangladesh

4 Department of Journalism, Media and Communications, Daffodil International University, Dhaka, Bangladesh

Haider Mahmood

5 Department of Finance, College of Business Administration, Prince Sattam Bin Abdulaziz University, 173, Alkharj, 11942 Saudi Arabia

Ijaz Younis

Associated data.

Data sources and relevant links are provided in the paper to access data.

Climate change is a long-lasting change in the weather arrays across tropics to polls. It is a global threat that has embarked on to put stress on various sectors. This study is aimed to conceptually engineer how climate variability is deteriorating the sustainability of diverse sectors worldwide. Specifically, the agricultural sector’s vulnerability is a globally concerning scenario, as sufficient production and food supplies are threatened due to irreversible weather fluctuations. In turn, it is challenging the global feeding patterns, particularly in countries with agriculture as an integral part of their economy and total productivity. Climate change has also put the integrity and survival of many species at stake due to shifts in optimum temperature ranges, thereby accelerating biodiversity loss by progressively changing the ecosystem structures. Climate variations increase the likelihood of particular food and waterborne and vector-borne diseases, and a recent example is a coronavirus pandemic. Climate change also accelerates the enigma of antimicrobial resistance, another threat to human health due to the increasing incidence of resistant pathogenic infections. Besides, the global tourism industry is devastated as climate change impacts unfavorable tourism spots. The methodology investigates hypothetical scenarios of climate variability and attempts to describe the quality of evidence to facilitate readers’ careful, critical engagement. Secondary data is used to identify sustainability issues such as environmental, social, and economic viability. To better understand the problem, gathered the information in this report from various media outlets, research agencies, policy papers, newspapers, and other sources. This review is a sectorial assessment of climate change mitigation and adaptation approaches worldwide in the aforementioned sectors and the associated economic costs. According to the findings, government involvement is necessary for the country’s long-term development through strict accountability of resources and regulations implemented in the past to generate cutting-edge climate policy. Therefore, mitigating the impacts of climate change must be of the utmost importance, and hence, this global threat requires global commitment to address its dreadful implications to ensure global sustenance.


Worldwide observed and anticipated climatic changes for the twenty-first century and global warming are significant global changes that have been encountered during the past 65 years. Climate change (CC) is an inter-governmental complex challenge globally with its influence over various components of the ecological, environmental, socio-political, and socio-economic disciplines (Adger et al.  2005 ; Leal Filho et al.  2021 ; Feliciano et al.  2022 ). Climate change involves heightened temperatures across numerous worlds (Battisti and Naylor  2009 ; Schuurmans  2021 ; Weisheimer and Palmer  2005 ; Yadav et al.  2015 ). With the onset of the industrial revolution, the problem of earth climate was amplified manifold (Leppänen et al.  2014 ). It is reported that the immediate attention and due steps might increase the probability of overcoming its devastating impacts. It is not plausible to interpret the exact consequences of climate change (CC) on a sectoral basis (Izaguirre et al.  2021 ; Jurgilevich et al.  2017 ), which is evident by the emerging level of recognition plus the inclusion of climatic uncertainties at both local and national level of policymaking (Ayers et al.  2014 ).

Climate change is characterized based on the comprehensive long-haul temperature and precipitation trends and other components such as pressure and humidity level in the surrounding environment. Besides, the irregular weather patterns, retreating of global ice sheets, and the corresponding elevated sea level rise are among the most renowned international and domestic effects of climate change (Lipczynska-Kochany  2018 ; Michel et al.  2021 ; Murshed and Dao 2020 ). Before the industrial revolution, natural sources, including volcanoes, forest fires, and seismic activities, were regarded as the distinct sources of greenhouse gases (GHGs) such as CO 2 , CH 4 , N 2 O, and H 2 O into the atmosphere (Murshed et al. 2020 ; Hussain et al.  2020 ; Sovacool et al.  2021 ; Usman and Balsalobre-Lorente 2022 ; Murshed 2022 ). United Nations Framework Convention on Climate Change (UNFCCC) struck a major agreement to tackle climate change and accelerate and intensify the actions and investments required for a sustainable low-carbon future at Conference of the Parties (COP-21) in Paris on December 12, 2015. The Paris Agreement expands on the Convention by bringing all nations together for the first time in a single cause to undertake ambitious measures to prevent climate change and adapt to its impacts, with increased funding to assist developing countries in doing so. As so, it marks a turning point in the global climate fight. The core goal of the Paris Agreement is to improve the global response to the threat of climate change by keeping the global temperature rise this century well below 2 °C over pre-industrial levels and to pursue efforts to limit the temperature increase to 1.5° C (Sharma et al. 2020 ; Sharif et al. 2020 ; Chien et al. 2021 .

Furthermore, the agreement aspires to strengthen nations’ ability to deal with the effects of climate change and align financing flows with low GHG emissions and climate-resilient paths (Shahbaz et al. 2019 ; Anwar et al. 2021 ; Usman et al. 2022a ). To achieve these lofty goals, adequate financial resources must be mobilized and provided, as well as a new technology framework and expanded capacity building, allowing developing countries and the most vulnerable countries to act under their respective national objectives. The agreement also establishes a more transparent action and support mechanism. All Parties are required by the Paris Agreement to do their best through “nationally determined contributions” (NDCs) and to strengthen these efforts in the coming years (Balsalobre-Lorente et al. 2020 ). It includes obligations that all Parties regularly report on their emissions and implementation activities. A global stock-take will be conducted every five years to review collective progress toward the agreement’s goal and inform the Parties’ future individual actions. The Paris Agreement became available for signature on April 22, 2016, Earth Day, at the United Nations Headquarters in New York. On November 4, 2016, it went into effect 30 days after the so-called double threshold was met (ratification by 55 nations accounting for at least 55% of world emissions). More countries have ratified and continue to ratify the agreement since then, bringing 125 Parties in early 2017. To fully operationalize the Paris Agreement, a work program was initiated in Paris to define mechanisms, processes, and recommendations on a wide range of concerns (Murshed et al. 2021 ). Since 2016, Parties have collaborated in subsidiary bodies (APA, SBSTA, and SBI) and numerous formed entities. The Conference of the Parties functioning as the meeting of the Parties to the Paris Agreement (CMA) convened for the first time in November 2016 in Marrakesh in conjunction with COP22 and made its first two resolutions. The work plan is scheduled to be finished by 2018. Some mitigation and adaptation strategies to reduce the emission in the prospective of Paris agreement are following firstly, a long-term goal of keeping the increase in global average temperature to well below 2 °C above pre-industrial levels, secondly, to aim to limit the rise to 1.5 °C, since this would significantly reduce risks and the impacts of climate change, thirdly, on the need for global emissions to peak as soon as possible, recognizing that this will take longer for developing countries, lastly, to undertake rapid reductions after that under the best available science, to achieve a balance between emissions and removals in the second half of the century. On the other side, some adaptation strategies are; strengthening societies’ ability to deal with the effects of climate change and to continue & expand international assistance for developing nations’ adaptation.

However, anthropogenic activities are currently regarded as most accountable for CC (Murshed et al. 2022 ). Apart from the industrial revolution, other anthropogenic activities include excessive agricultural operations, which further involve the high use of fuel-based mechanization, burning of agricultural residues, burning fossil fuels, deforestation, national and domestic transportation sectors, etc. (Huang et al.  2016 ). Consequently, these anthropogenic activities lead to climatic catastrophes, damaging local and global infrastructure, human health, and total productivity. Energy consumption has mounted GHGs levels concerning warming temperatures as most of the energy production in developing countries comes from fossil fuels (Balsalobre-Lorente et al. 2022 ; Usman et al. 2022b ; Abbass et al. 2021a ; Ishikawa-Ishiwata and Furuya  2022 ).

This review aims to highlight the effects of climate change in a socio-scientific aspect by analyzing the existing literature on various sectorial pieces of evidence globally that influence the environment. Although this review provides a thorough examination of climate change and its severe affected sectors that pose a grave danger for global agriculture, biodiversity, health, economy, forestry, and tourism, and to purpose some practical prophylactic measures and mitigation strategies to be adapted as sound substitutes to survive from climate change (CC) impacts. The societal implications of irregular weather patterns and other effects of climate changes are discussed in detail. Some numerous sustainable mitigation measures and adaptation practices and techniques at the global level are discussed in this review with an in-depth focus on its economic, social, and environmental aspects. Methods of data collection section are included in the supplementary information.

Review methodology

Related study and its objectives.

Today, we live an ordinary life in the beautiful digital, globalized world where climate change has a decisive role. What happens in one country has a massive influence on geographically far apart countries, which points to the current crisis known as COVID-19 (Sarkar et al.  2021 ). The most dangerous disease like COVID-19 has affected the world’s climate changes and economic conditions (Abbass et al. 2022 ; Pirasteh-Anosheh et al.  2021 ). The purpose of the present study is to review the status of research on the subject, which is based on “Global Climate Change Impacts, adaptation, and sustainable mitigation measures” by systematically reviewing past published and unpublished research work. Furthermore, the current study seeks to comment on research on the same topic and suggest future research on the same topic. Specifically, the present study aims: The first one is, organize publications to make them easy and quick to find. Secondly, to explore issues in this area, propose an outline of research for future work. The third aim of the study is to synthesize the previous literature on climate change, various sectors, and their mitigation measurement. Lastly , classify the articles according to the different methods and procedures that have been adopted.

Review methodology for reviewers

This review-based article followed systematic literature review techniques that have proved the literature review as a rigorous framework (Benita  2021 ; Tranfield et al.  2003 ). Moreover, we illustrate in Fig.  1 the search method that we have started for this research. First, finalized the research theme to search literature (Cooper et al.  2018 ). Second, used numerous research databases to search related articles and download from the database (Web of Science, Google Scholar, Scopus Index Journals, Emerald, Elsevier Science Direct, Springer, and Sciverse). We focused on various articles, with research articles, feedback pieces, short notes, debates, and review articles published in scholarly journals. Reports used to search for multiple keywords such as “Climate Change,” “Mitigation and Adaptation,” “Department of Agriculture and Human Health,” “Department of Biodiversity and Forestry,” etc.; in summary, keyword list and full text have been made. Initially, the search for keywords yielded a large amount of literature.

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Methodology search for finalized articles for investigations.

Source : constructed by authors

Since 2020, it has been impossible to review all the articles found; some restrictions have been set for the literature exhibition. The study searched 95 articles on a different database mentioned above based on the nature of the study. It excluded 40 irrelevant papers due to copied from a previous search after readings tiles, abstract and full pieces. The criteria for inclusion were: (i) articles focused on “Global Climate Change Impacts, adaptation, and sustainable mitigation measures,” and (ii) the search key terms related to study requirements. The complete procedure yielded 55 articles for our study. We repeat our search on the “Web of Science and Google Scholars” database to enhance the search results and check the referenced articles.

In this study, 55 articles are reviewed systematically and analyzed for research topics and other aspects, such as the methods, contexts, and theories used in these studies. Furthermore, this study analyzes closely related areas to provide unique research opportunities in the future. The study also discussed future direction opportunities and research questions by understanding the research findings climate changes and other affected sectors. The reviewed paper framework analysis process is outlined in Fig.  2 .

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Framework of the analysis Process.

Natural disasters and climate change’s socio-economic consequences

Natural and environmental disasters can be highly variable from year to year; some years pass with very few deaths before a significant disaster event claims many lives (Symanski et al.  2021 ). Approximately 60,000 people globally died from natural disasters each year on average over the past decade (Ritchie and Roser  2014 ; Wiranata and Simbolon  2021 ). So, according to the report, around 0.1% of global deaths. Annual variability in the number and share of deaths from natural disasters in recent decades are shown in Fig.  3 . The number of fatalities can be meager—sometimes less than 10,000, and as few as 0.01% of all deaths. But shock events have a devastating impact: the 1983–1985 famine and drought in Ethiopia; the 2004 Indian Ocean earthquake and tsunami; Cyclone Nargis, which struck Myanmar in 2008; and the 2010 Port-au-Prince earthquake in Haiti and now recent example is COVID-19 pandemic (Erman et al.  2021 ). These events pushed global disaster deaths to over 200,000—more than 0.4% of deaths in these years. Low-frequency, high-impact events such as earthquakes and tsunamis are not preventable, but such high losses of human life are. Historical evidence shows that earlier disaster detection, more robust infrastructure, emergency preparedness, and response programmers have substantially reduced disaster deaths worldwide. Low-income is also the most vulnerable to disasters; improving living conditions, facilities, and response services in these areas would be critical in reducing natural disaster deaths in the coming decades.

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Global deaths from natural disasters, 1978 to 2020.

Source EMDAT ( 2020 )

The interior regions of the continent are likely to be impacted by rising temperatures (Dimri et al.  2018 ; Goes et al.  2020 ; Mannig et al.  2018 ; Schuurmans  2021 ). Weather patterns change due to the shortage of natural resources (water), increase in glacier melting, and rising mercury are likely to cause extinction to many planted species (Gampe et al.  2016 ; Mihiretu et al.  2021 ; Shaffril et al.  2018 ).On the other hand, the coastal ecosystem is on the verge of devastation (Perera et al.  2018 ; Phillips  2018 ). The temperature rises, insect disease outbreaks, health-related problems, and seasonal and lifestyle changes are persistent, with a strong probability of these patterns continuing in the future (Abbass et al. 2021c ; Hussain et al.  2018 ). At the global level, a shortage of good infrastructure and insufficient adaptive capacity are hammering the most (IPCC  2013 ). In addition to the above concerns, a lack of environmental education and knowledge, outdated consumer behavior, a scarcity of incentives, a lack of legislation, and the government’s lack of commitment to climate change contribute to the general public’s concerns. By 2050, a 2 to 3% rise in mercury and a drastic shift in rainfall patterns may have serious consequences (Huang et al. 2022 ; Gorst et al.  2018 ). Natural and environmental calamities caused huge losses globally, such as decreased agriculture outputs, rehabilitation of the system, and rebuilding necessary technologies (Ali and Erenstein  2017 ; Ramankutty et al.  2018 ; Yu et al.  2021 ) (Table ​ (Table1). 1 ). Furthermore, in the last 3 or 4 years, the world has been plagued by smog-related eye and skin diseases, as well as a rise in road accidents due to poor visibility.

Main natural danger statistics for 1985–2020 at the global level

Key natural hazards statistics from 1978 to 2020
Country1978 change2018Absolute changeRelative
Drought630 − 63 − 100%
Earthquake25,1624,321 − 20,841 − 83%
Extreme temperature150536 + 386 + 257%
Extreme weather36761,666 − 2,010 − 55%
Flood5,8972,869 − 3,028 − 51%
Landslide86275 + 189 + 220%
Mass movement5017 − 33 − 66%
Volcanic activity268878 + 610 + 228%
Wildfire2247 + 245 + 12,250%
All − natural disasters35,03610,809 − 24,227 − 69%

Source: EM-DAT ( 2020 )

Climate change and agriculture

Global agriculture is the ultimate sector responsible for 30–40% of all greenhouse emissions, which makes it a leading industry predominantly contributing to climate warming and significantly impacted by it (Grieg; Mishra et al.  2021 ; Ortiz et al.  2021 ; Thornton and Lipper  2014 ). Numerous agro-environmental and climatic factors that have a dominant influence on agriculture productivity (Pautasso et al.  2012 ) are significantly impacted in response to precipitation extremes including floods, forest fires, and droughts (Huang  2004 ). Besides, the immense dependency on exhaustible resources also fuels the fire and leads global agriculture to become prone to devastation. Godfray et al. ( 2010 ) mentioned that decline in agriculture challenges the farmer’s quality of life and thus a significant factor to poverty as the food and water supplies are critically impacted by CC (Ortiz et al.  2021 ; Rosenzweig et al.  2014 ). As an essential part of the economic systems, especially in developing countries, agricultural systems affect the overall economy and potentially the well-being of households (Schlenker and Roberts  2009 ). According to the report published by the Intergovernmental Panel on Climate Change (IPCC), atmospheric concentrations of greenhouse gases, i.e., CH 4, CO 2 , and N 2 O, are increased in the air to extraordinary levels over the last few centuries (Usman and Makhdum 2021 ; Stocker et al.  2013 ). Climate change is the composite outcome of two different factors. The first is the natural causes, and the second is the anthropogenic actions (Karami 2012 ). It is also forecasted that the world may experience a typical rise in temperature stretching from 1 to 3.7 °C at the end of this century (Pachauri et al. 2014 ). The world’s crop production is also highly vulnerable to these global temperature-changing trends as raised temperatures will pose severe negative impacts on crop growth (Reidsma et al. 2009 ). Some of the recent modeling about the fate of global agriculture is briefly described below.

Decline in cereal productivity

Crop productivity will also be affected dramatically in the next few decades due to variations in integral abiotic factors such as temperature, solar radiation, precipitation, and CO 2 . These all factors are included in various regulatory instruments like progress and growth, weather-tempted changes, pest invasions (Cammell and Knight 1992 ), accompanying disease snags (Fand et al. 2012 ), water supplies (Panda et al. 2003 ), high prices of agro-products in world’s agriculture industry, and preeminent quantity of fertilizer consumption. Lobell and field ( 2007 ) claimed that from 1962 to 2002, wheat crop output had condensed significantly due to rising temperatures. Therefore, during 1980–2011, the common wheat productivity trends endorsed extreme temperature events confirmed by Gourdji et al. ( 2013 ) around South Asia, South America, and Central Asia. Various other studies (Asseng, Cao, Zhang, and Ludwig 2009 ; Asseng et al. 2013 ; García et al. 2015 ; Ortiz et al. 2021 ) also proved that wheat output is negatively affected by the rising temperatures and also caused adverse effects on biomass productivity (Calderini et al. 1999 ; Sadras and Slafer 2012 ). Hereafter, the rice crop is also influenced by the high temperatures at night. These difficulties will worsen because the temperature will be rising further in the future owing to CC (Tebaldi et al. 2006 ). Another research conducted in China revealed that a 4.6% of rice production per 1 °C has happened connected with the advancement in night temperatures (Tao et al. 2006 ). Moreover, the average night temperature growth also affected rice indicia cultivar’s output pragmatically during 25 years in the Philippines (Peng et al. 2004 ). It is anticipated that the increase in world average temperature will also cause a substantial reduction in yield (Hatfield et al. 2011 ; Lobell and Gourdji 2012 ). In the southern hemisphere, Parry et al. ( 2007 ) noted a rise of 1–4 °C in average daily temperatures at the end of spring season unti the middle of summers, and this raised temperature reduced crop output by cutting down the time length for phenophases eventually reduce the yield (Hatfield and Prueger 2015 ; R. Ortiz 2008 ). Also, world climate models have recommended that humid and subtropical regions expect to be plentiful prey to the upcoming heat strokes (Battisti and Naylor 2009 ). Grain production is the amalgamation of two constituents: the average weight and the grain output/m 2 , however, in crop production. Crop output is mainly accredited to the grain quantity (Araus et al. 2008 ; Gambín and Borrás 2010 ). In the times of grain set, yield resources are mainly strewn between hitherto defined components, i.e., grain usual weight and grain output, which presents a trade-off between them (Gambín and Borrás 2010 ) beside disparities in per grain integration (B. L. Gambín et al. 2006 ). In addition to this, the maize crop is also susceptible to raised temperatures, principally in the flowering stage (Edreira and Otegui 2013 ). In reality, the lower grain number is associated with insufficient acclimatization due to intense photosynthesis and higher respiration and the high-temperature effect on the reproduction phenomena (Edreira and Otegui 2013 ). During the flowering phase, maize visible to heat (30–36 °C) seemed less anthesis-silking intermissions (Edreira et al. 2011 ). Another research by Dupuis and Dumas ( 1990 ) proved that a drop in spikelet when directly visible to high temperatures above 35 °C in vitro pollination. Abnormalities in kernel number claimed by Vega et al. ( 2001 ) is related to conceded plant development during a flowering phase that is linked with the active ear growth phase and categorized as a critical phase for approximation of kernel number during silking (Otegui and Bonhomme 1998 ).

The retort of rice output to high temperature presents disparities in flowering patterns, and seed set lessens and lessens grain weight (Qasim et al. 2020 ; Qasim, Hammad, Maqsood, Tariq, & Chawla). During the daytime, heat directly impacts flowers which lessens the thesis period and quickens the earlier peak flowering (Tao et al. 2006 ). Antagonistic effect of higher daytime temperature d on pollen sprouting proposed seed set decay, whereas, seed set was lengthily reduced than could be explicated by pollen growing at high temperatures 40◦C (Matsui et al. 2001 ).

The decline in wheat output is linked with higher temperatures, confirmed in numerous studies (Semenov 2009 ; Stone and Nicolas 1994 ). High temperatures fast-track the arrangements of plant expansion (Blum et al. 2001 ), diminution photosynthetic process (Salvucci and Crafts‐Brandner 2004 ), and also considerably affect the reproductive operations (Farooq et al. 2011 ).

The destructive impacts of CC induced weather extremes to deteriorate the integrity of crops (Chaudhary et al. 2011 ), e.g., Spartan cold and extreme fog cause falling and discoloration of betel leaves (Rosenzweig et al. 2001 ), giving them a somehow reddish appearance, squeezing of lemon leaves (Pautasso et al. 2012 ), as well as root rot of pineapple, have reported (Vedwan and Rhoades 2001 ). Henceforth, in tackling the disruptive effects of CC, several short-term and long-term management approaches are the crucial need of time (Fig.  4 ). Moreover, various studies (Chaudhary et al. 2011 ; Patz et al. 2005 ; Pautasso et al. 2012 ) have demonstrated adapting trends such as ameliorating crop diversity can yield better adaptability towards CC.

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Schematic description of potential impacts of climate change on the agriculture sector and the appropriate mitigation and adaptation measures to overcome its impact.

Climate change impacts on biodiversity

Global biodiversity is among the severe victims of CC because it is the fastest emerging cause of species loss. Studies demonstrated that the massive scale species dynamics are considerably associated with diverse climatic events (Abraham and Chain 1988 ; Manes et al. 2021 ; A. M. D. Ortiz et al. 2021 ). Both the pace and magnitude of CC are altering the compatible habitat ranges for living entities of marine, freshwater, and terrestrial regions. Alterations in general climate regimes influence the integrity of ecosystems in numerous ways, such as variation in the relative abundance of species, range shifts, changes in activity timing, and microhabitat use (Bates et al. 2014 ). The geographic distribution of any species often depends upon its ability to tolerate environmental stresses, biological interactions, and dispersal constraints. Hence, instead of the CC, the local species must only accept, adapt, move, or face extinction (Berg et al. 2010 ). So, the best performer species have a better survival capacity for adjusting to new ecosystems or a decreased perseverance to survive where they are already situated (Bates et al. 2014 ). An important aspect here is the inadequate habitat connectivity and access to microclimates, also crucial in raising the exposure to climate warming and extreme heatwave episodes. For example, the carbon sequestration rates are undergoing fluctuations due to climate-driven expansion in the range of global mangroves (Cavanaugh et al. 2014 ).

Similarly, the loss of kelp-forest ecosystems in various regions and its occupancy by the seaweed turfs has set the track for elevated herbivory by the high influx of tropical fish populations. Not only this, the increased water temperatures have exacerbated the conditions far away from the physiological tolerance level of the kelp communities (Vergés et al. 2016 ; Wernberg et al. 2016 ). Another pertinent danger is the devastation of keystone species, which even has more pervasive effects on the entire communities in that habitat (Zarnetske et al. 2012 ). It is particularly important as CC does not specify specific populations or communities. Eventually, this CC-induced redistribution of species may deteriorate carbon storage and the net ecosystem productivity (Weed et al. 2013 ). Among the typical disruptions, the prominent ones include impacts on marine and terrestrial productivity, marine community assembly, and the extended invasion of toxic cyanobacteria bloom (Fossheim et al. 2015 ).

The CC-impacted species extinction is widely reported in the literature (Beesley et al. 2019 ; Urban 2015 ), and the predictions of demise until the twenty-first century are dreadful (Abbass et al. 2019 ; Pereira et al. 2013 ). In a few cases, northward shifting of species may not be formidable as it allows mountain-dwelling species to find optimum climates. However, the migrant species may be trapped in isolated and incompatible habitats due to losing topography and range (Dullinger et al. 2012 ). For example, a study indicated that the American pika has been extirpated or intensely diminished in some regions, primarily attributed to the CC-impacted extinction or at least local extirpation (Stewart et al. 2015 ). Besides, the anticipation of persistent responses to the impacts of CC often requires data records of several decades to rigorously analyze the critical pre and post CC patterns at species and ecosystem levels (Manes et al. 2021 ; Testa et al. 2018 ).

Nonetheless, the availability of such long-term data records is rare; hence, attempts are needed to focus on these profound aspects. Biodiversity is also vulnerable to the other associated impacts of CC, such as rising temperatures, droughts, and certain invasive pest species. For instance, a study revealed the changes in the composition of plankton communities attributed to rising temperatures. Henceforth, alterations in such aquatic producer communities, i.e., diatoms and calcareous plants, can ultimately lead to variation in the recycling of biological carbon. Moreover, such changes are characterized as a potential contributor to CO 2 differences between the Pleistocene glacial and interglacial periods (Kohfeld et al. 2005 ).

Climate change implications on human health

It is an understood corporality that human health is a significant victim of CC (Costello et al. 2009 ). According to the WHO, CC might be responsible for 250,000 additional deaths per year during 2030–2050 (Watts et al. 2015 ). These deaths are attributed to extreme weather-induced mortality and morbidity and the global expansion of vector-borne diseases (Lemery et al. 2021; Yang and Usman 2021 ; Meierrieks 2021 ; UNEP 2017 ). Here, some of the emerging health issues pertinent to this global problem are briefly described.

Climate change and antimicrobial resistance with corresponding economic costs

Antimicrobial resistance (AMR) is an up-surging complex global health challenge (Garner et al. 2019 ; Lemery et al. 2021 ). Health professionals across the globe are extremely worried due to this phenomenon that has critical potential to reverse almost all the progress that has been achieved so far in the health discipline (Gosling and Arnell 2016 ). A massive amount of antibiotics is produced by many pharmaceutical industries worldwide, and the pathogenic microorganisms are gradually developing resistance to them, which can be comprehended how strongly this aspect can shake the foundations of national and global economies (UNEP 2017 ). This statement is supported by the fact that AMR is not developing in a particular region or country. Instead, it is flourishing in every continent of the world (WHO 2018 ). This plague is heavily pushing humanity to the post-antibiotic era, in which currently antibiotic-susceptible pathogens will once again lead to certain endemics and pandemics after being resistant(WHO 2018 ). Undesirably, if this statement would become a factuality, there might emerge certain risks in undertaking sophisticated interventions such as chemotherapy, joint replacement cases, and organ transplantation (Su et al. 2018 ). Presently, the amplification of drug resistance cases has made common illnesses like pneumonia, post-surgical infections, HIV/AIDS, tuberculosis, malaria, etc., too difficult and costly to be treated or cure well (WHO 2018 ). From a simple example, it can be assumed how easily antibiotic-resistant strains can be transmitted from one person to another and ultimately travel across the boundaries (Berendonk et al. 2015 ). Talking about the second- and third-generation classes of antibiotics, e.g., most renowned generations of cephalosporin antibiotics that are more expensive, broad-spectrum, more toxic, and usually require more extended periods whenever prescribed to patients (Lemery et al. 2021 ; Pärnänen et al. 2019 ). This scenario has also revealed that the abundance of resistant strains of pathogens was also higher in the Southern part (WHO 2018 ). As southern parts are generally warmer than their counterparts, it is evident from this example how CC-induced global warming can augment the spread of antibiotic-resistant strains within the biosphere, eventually putting additional economic burden in the face of developing new and costlier antibiotics. The ARG exchange to susceptible bacteria through one of the potential mechanisms, transformation, transduction, and conjugation; Selection pressure can be caused by certain antibiotics, metals or pesticides, etc., as shown in Fig.  5 .

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A typical interaction between the susceptible and resistant strains.

Source: Elsayed et al. ( 2021 ); Karkman et al. ( 2018 )

Certain studies highlighted that conventional urban wastewater treatment plants are typical hotspots where most bacterial strains exchange genetic material through horizontal gene transfer (Fig.  5 ). Although at present, the extent of risks associated with the antibiotic resistance found in wastewater is complicated; environmental scientists and engineers have particular concerns about the potential impacts of these antibiotic resistance genes on human health (Ashbolt 2015 ). At most undesirable and worst case, these antibiotic-resistant genes containing bacteria can make their way to enter into the environment (Pruden et al. 2013 ), irrigation water used for crops and public water supplies and ultimately become a part of food chains and food webs (Ma et al. 2019 ; D. Wu et al. 2019 ). This problem has been reported manifold in several countries (Hendriksen et al. 2019 ), where wastewater as a means of irrigated water is quite common.

Climate change and vector borne-diseases

Temperature is a fundamental factor for the sustenance of living entities regardless of an ecosystem. So, a specific living being, especially a pathogen, requires a sophisticated temperature range to exist on earth. The second essential component of CC is precipitation, which also impacts numerous infectious agents’ transport and dissemination patterns. Global rising temperature is a significant cause of many species extinction. On the one hand, this changing environmental temperature may be causing species extinction, and on the other, this warming temperature might favor the thriving of some new organisms. Here, it was evident that some pathogens may also upraise once non-evident or reported (Patz et al. 2000 ). This concept can be exemplified through certain pathogenic strains of microorganisms that how the likelihood of various diseases increases in response to climate warming-induced environmental changes (Table ​ (Table2 2 ).

Examples of how various environmental changes affect various infectious diseases in humans

Environmental modificationsPotential diseasesThe causative organisms and pathway of effect
Construction of canals, dams, irrigation pathwaysSchistosomiasisSnail host locale, human contact
MalariaUpbringing places for mosquitoes
HelminthiasesLarval contact due to moist soil
River blindnessBlackfly upbringing
Agro-strengtheningMalariaCrop pesticides
Venezuelan hemorrhagic feverRodent abundance, contact
SuburbanizationCholeradeprived hygiene, asepsis; augmented water municipal assembling pollution
DengueWater-gathering rubbishes Aedes aegypti mosquito upbringing sites
Cutaneous leishmaniasisPSandfly vectors
Deforestation and new tenancyMalariaUpbringing sites and trajectories, migration of vulnerable people
Oropoucheupsurge contact, upbringing of directions
Visceral leishmaniasisRecurrent contact with sandfly vectors
AgricultureLyme diseaseTick hosts, outside revelation
Ocean heatingRed tidePoisonous algal blooms

Source: Aron and Patz ( 2001 )

A recent example is an outburst of coronavirus (COVID-19) in the Republic of China, causing pneumonia and severe acute respiratory complications (Cui et al. 2021 ; Song et al. 2021 ). The large family of viruses is harbored in numerous animals, bats, and snakes in particular ( with the subsequent transfer into human beings. Hence, it is worth noting that the thriving of numerous vectors involved in spreading various diseases is influenced by Climate change (Ogden 2018 ; Santos et al. 2021 ).

Psychological impacts of climate change

Climate change (CC) is responsible for the rapid dissemination and exaggeration of certain epidemics and pandemics. In addition to the vast apparent impacts of climate change on health, forestry, agriculture, etc., it may also have psychological implications on vulnerable societies. It can be exemplified through the recent outburst of (COVID-19) in various countries around the world (Pal 2021 ). Besides, the victims of this viral infection have made healthy beings scarier and terrified. In the wake of such epidemics, people with common colds or fever are also frightened and must pass specific regulatory protocols. Living in such situations continuously terrifies the public and makes the stress familiar, which eventually makes them psychologically weak (

CC boosts the extent of anxiety, distress, and other issues in public, pushing them to develop various mental-related problems. Besides, frequent exposure to extreme climatic catastrophes such as geological disasters also imprints post-traumatic disorder, and their ubiquitous occurrence paves the way to developing chronic psychological dysfunction. Moreover, repetitive listening from media also causes an increase in the person’s stress level (Association 2020 ). Similarly, communities living in flood-prone areas constantly live in extreme fear of drowning and die by floods. In addition to human lives, the flood-induced destruction of physical infrastructure is a specific reason for putting pressure on these communities (Ogden 2018 ). For instance, Ogden ( 2018 ) comprehensively denoted that Katrina’s Hurricane augmented the mental health issues in the victim communities.

Climate change impacts on the forestry sector

Forests are the global regulators of the world’s climate (FAO 2018 ) and have an indispensable role in regulating global carbon and nitrogen cycles (Rehman et al. 2021 ; Reichstein and Carvalhais 2019 ). Hence, disturbances in forest ecology affect the micro and macro-climates (Ellison et al. 2017 ). Climate warming, in return, has profound impacts on the growth and productivity of transboundary forests by influencing the temperature and precipitation patterns, etc. As CC induces specific changes in the typical structure and functions of ecosystems (Zhang et al. 2017 ) as well impacts forest health, climate change also has several devastating consequences such as forest fires, droughts, pest outbreaks (EPA 2018 ), and last but not the least is the livelihoods of forest-dependent communities. The rising frequency and intensity of another CC product, i.e., droughts, pose plenty of challenges to the well-being of global forests (Diffenbaugh et al. 2017 ), which is further projected to increase soon (Hartmann et al. 2018 ; Lehner et al. 2017 ; Rehman et al. 2021 ). Hence, CC induces storms, with more significant impacts also put extra pressure on the survival of the global forests (Martínez-Alvarado et al. 2018 ), significantly since their influences are augmented during higher winter precipitations with corresponding wetter soils causing weak root anchorage of trees (Brázdil et al. 2018 ). Surging temperature regimes causes alterations in usual precipitation patterns, which is a significant hurdle for the survival of temperate forests (Allen et al. 2010 ; Flannigan et al. 2013 ), letting them encounter severe stress and disturbances which adversely affects the local tree species (Hubbart et al. 2016 ; Millar and Stephenson 2015 ; Rehman et al. 2021 ).

Climate change impacts on forest-dependent communities

Forests are the fundamental livelihood resource for about 1.6 billion people worldwide; out of them, 350 million are distinguished with relatively higher reliance (Bank 2008 ). Agro-forestry-dependent communities comprise 1.2 billion, and 60 million indigenous people solely rely on forests and their products to sustain their lives (Sunderlin et al. 2005 ). For example, in the entire African continent, more than 2/3rd of inhabitants depend on forest resources and woodlands for their alimonies, e.g., food, fuelwood and grazing (Wasiq and Ahmad 2004 ). The livings of these people are more intensely affected by the climatic disruptions making their lives harder (Brown et al. 2014 ). On the one hand, forest communities are incredibly vulnerable to CC due to their livelihoods, cultural and spiritual ties as well as socio-ecological connections, and on the other, they are not familiar with the term “climate change.” (Rahman and Alam 2016 ). Among the destructive impacts of temperature and rainfall, disruption of the agroforestry crops with resultant downscale growth and yield (Macchi et al. 2008 ). Cruz ( 2015 ) ascribed that forest-dependent smallholder farmers in the Philippines face the enigma of delayed fruiting, more severe damages by insect and pest incidences due to unfavorable temperature regimes, and changed rainfall patterns.

Among these series of challenges to forest communities, their well-being is also distinctly vulnerable to CC. Though the detailed climate change impacts on human health have been comprehensively mentioned in the previous section, some studies have listed a few more devastating effects on the prosperity of forest-dependent communities. For instance, the Himalayan people have been experiencing frequent skin-borne diseases such as malaria and other skin diseases due to increasing mosquitoes, wild boar as well, and new wasps species, particularly in higher altitudes that were almost non-existent before last 5–10 years (Xu et al. 2008 ). Similarly, people living at high altitudes in Bangladesh have experienced frequent mosquito-borne calamities (Fardous; Sharma 2012 ). In addition, the pace of other waterborne diseases such as infectious diarrhea, cholera, pathogenic induced abdominal complications and dengue has also been boosted in other distinguished regions of Bangladesh (Cell 2009 ; Gunter et al. 2008 ).

Pest outbreak

Upscaling hotter climate may positively affect the mobile organisms with shorter generation times because they can scurry from harsh conditions than the immobile species (Fettig et al. 2013 ; Schoene and Bernier 2012 ) and are also relatively more capable of adapting to new environments (Jactel et al. 2019 ). It reveals that insects adapt quickly to global warming due to their mobility advantages. Due to past outbreaks, the trees (forests) are relatively more susceptible victims (Kurz et al. 2008 ). Before CC, the influence of factors mentioned earlier, i.e., droughts and storms, was existent and made the forests susceptible to insect pest interventions; however, the global forests remain steadfast, assiduous, and green (Jactel et al. 2019 ). The typical reasons could be the insect herbivores were regulated by several tree defenses and pressures of predation (Wilkinson and Sherratt 2016 ). As climate greatly influences these phenomena, the global forests cannot be so sedulous against such challenges (Jactel et al. 2019 ). Table ​ Table3 3 demonstrates some of the particular considerations with practical examples that are essential while mitigating the impacts of CC in the forestry sector.

Essential considerations while mitigating the climate change impacts on the forestry sector

AttributesDescriptionForestry example
PurposefulnessAutonomousIncludes continuing application of prevailing information and techniques in retort to experienced climate change

Thin to reduce drought stress; construct breaks in vegetation to

Stop feast of wildfires, vermin, and ailments

TimingPreemptiveNecessitates interactive change to diminish future injury, jeopardy, and weakness, often through planning, observing, growing consciousness, structure partnerships, and ornamental erudition or investigation

Ensure forest property against potential future losses; transition to

species or stand erections that are better reformed to predictable

future conditions; trial with new forestry organization



Involves making small changes in present circumstances to circumvent disturbances

and ongoing to chase the same purposes

Condense rotation pauses to decrease the likelihood of harm to storm Events, differentiate classes to blowout jeopardy; thin to lessening compactness and defenselessness of jungle stands to tension
GoalOppositionShield or defend from alteration; take procedures to reservation constancy and battle changeGenerate refugia for rare classes; defend woodlands from austere fire and wind uproar; alter forest construction to reduce harshness or extent of wind and ice impairment; establish breaks in vegetation to dampen the spread of vermin, ailments, and wildfire

Source : Fischer ( 2019 )

Climate change impacts on tourism

Tourism is a commercial activity that has roots in multi-dimensions and an efficient tool with adequate job generation potential, revenue creation, earning of spectacular foreign exchange, enhancement in cross-cultural promulgation and cooperation, a business tool for entrepreneurs and eventually for the country’s national development (Arshad et al. 2018 ; Scott 2021 ). Among a plethora of other disciplines, the tourism industry is also a distinct victim of climate warming (Gössling et al. 2012 ; Hall et al. 2015 ) as the climate is among the essential resources that enable tourism in particular regions as most preferred locations. Different places at different times of the year attract tourists both within and across the countries depending upon the feasibility and compatibility of particular weather patterns. Hence, the massive variations in these weather patterns resulting from CC will eventually lead to monumental challenges to the local economy in that specific area’s particular and national economy (Bujosa et al. 2015 ). For instance, the Intergovernmental Panel on Climate Change (IPCC) report demonstrated that the global tourism industry had faced a considerable decline in the duration of ski season, including the loss of some ski areas and the dramatic shifts in tourist destinations’ climate warming.

Furthermore, different studies (Neuvonen et al. 2015 ; Scott et al. 2004 ) indicated that various currently perfect tourist spots, e.g., coastal areas, splendid islands, and ski resorts, will suffer consequences of CC. It is also worth noting that the quality and potential of administrative management potential to cope with the influence of CC on the tourism industry is of crucial significance, which renders specific strengths of resiliency to numerous destinations to withstand against it (Füssel and Hildén 2014 ). Similarly, in the partial or complete absence of adequate socio-economic and socio-political capital, the high-demanding tourist sites scurry towards the verge of vulnerability. The susceptibility of tourism is based on different components such as the extent of exposure, sensitivity, life-supporting sectors, and capacity assessment factors (Füssel and Hildén 2014 ). It is obvious corporality that sectors such as health, food, ecosystems, human habitat, infrastructure, water availability, and the accessibility of a particular region are prone to CC. Henceforth, the sensitivity of these critical sectors to CC and, in return, the adaptive measures are a hallmark in determining the composite vulnerability of climate warming (Ionescu et al. 2009 ).

Moreover, the dependence on imported food items, poor hygienic conditions, and inadequate health professionals are dominant aspects affecting the local terrestrial and aquatic biodiversity. Meanwhile, the greater dependency on ecosystem services and its products also makes a destination more fragile to become a prey of CC (Rizvi et al. 2015 ). Some significant non-climatic factors are important indicators of a particular ecosystem’s typical health and functioning, e.g., resource richness and abundance portray the picture of ecosystem stability. Similarly, the species abundance is also a productive tool that ensures that the ecosystem has a higher buffering capacity, which is terrific in terms of resiliency (Roscher et al. 2013 ).

Climate change impacts on the economic sector

Climate plays a significant role in overall productivity and economic growth. Due to its increasingly global existence and its effect on economic growth, CC has become one of the major concerns of both local and international environmental policymakers (Ferreira et al. 2020 ; Gleditsch 2021 ; Abbass et al. 2021b ; Lamperti et al. 2021 ). The adverse effects of CC on the overall productivity factor of the agricultural sector are therefore significant for understanding the creation of local adaptation policies and the composition of productive climate policy contracts. Previous studies on CC in the world have already forecasted its effects on the agricultural sector. Researchers have found that global CC will impact the agricultural sector in different world regions. The study of the impacts of CC on various agrarian activities in other demographic areas and the development of relative strategies to respond to effects has become a focal point for researchers (Chandioet al. 2020 ; Gleditsch 2021 ; Mosavi et al. 2020 ).

With the rapid growth of global warming since the 1980s, the temperature has started increasing globally, which resulted in the incredible transformation of rain and evaporation in the countries. The agricultural development of many countries has been reliant, delicate, and susceptible to CC for a long time, and it is on the development of agriculture total factor productivity (ATFP) influence different crops and yields of farmers (Alhassan 2021 ; Wu  2020 ).

Food security and natural disasters are increasing rapidly in the world. Several major climatic/natural disasters have impacted local crop production in the countries concerned. The effects of these natural disasters have been poorly controlled by the development of the economies and populations and may affect human life as well. One example is China, which is among the world’s most affected countries, vulnerable to natural disasters due to its large population, harsh environmental conditions, rapid CC, low environmental stability, and disaster power. According to the January 2016 statistical survey, China experienced an economic loss of 298.3 billion Yuan, and about 137 million Chinese people were severely affected by various natural disasters (Xie et al. 2018 ).

Mitigation and adaptation strategies of climate changes

Adaptation and mitigation are the crucial factors to address the response to CC (Jahanzad et al. 2020 ). Researchers define mitigation on climate changes, and on the other hand, adaptation directly impacts climate changes like floods. To some extent, mitigation reduces or moderates greenhouse gas emission, and it becomes a critical issue both economically and environmentally (Botzen et al. 2021 ; Jahanzad et al. 2020 ; Kongsager 2018 ; Smit et al. 2000 ; Vale et al. 2021 ; Usman et al. 2021 ; Verheyen 2005 ).

Researchers have deep concern about the adaptation and mitigation methodologies in sectoral and geographical contexts. Agriculture, industry, forestry, transport, and land use are the main sectors to adapt and mitigate policies(Kärkkäinen et al. 2020 ; Waheed et al. 2021 ). Adaptation and mitigation require particular concern both at the national and international levels. The world has faced a significant problem of climate change in the last decades, and adaptation to these effects is compulsory for economic and social development. To adapt and mitigate against CC, one should develop policies and strategies at the international level (Hussain et al. 2020 ). Figure  6 depicts the list of current studies on sectoral impacts of CC with adaptation and mitigation measures globally.

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Sectoral impacts of climate change with adaptation and mitigation measures.

Conclusion and future perspectives

Specific socio-agricultural, socio-economic, and physical systems are the cornerstone of psychological well-being, and the alteration in these systems by CC will have disastrous impacts. Climate variability, alongside other anthropogenic and natural stressors, influences human and environmental health sustainability. Food security is another concerning scenario that may lead to compromised food quality, higher food prices, and inadequate food distribution systems. Global forests are challenged by different climatic factors such as storms, droughts, flash floods, and intense precipitation. On the other hand, their anthropogenic wiping is aggrandizing their existence. Undoubtedly, the vulnerability scale of the world’s regions differs; however, appropriate mitigation and adaptation measures can aid the decision-making bodies in developing effective policies to tackle its impacts. Presently, modern life on earth has tailored to consistent climatic patterns, and accordingly, adapting to such considerable variations is of paramount importance. Because the faster changes in climate will make it harder to survive and adjust, this globally-raising enigma calls for immediate attention at every scale ranging from elementary community level to international level. Still, much effort, research, and dedication are required, which is the most critical time. Some policy implications can help us to mitigate the consequences of climate change, especially the most affected sectors like the agriculture sector;

Warming might lengthen the season in frost-prone growing regions (temperate and arctic zones), allowing for longer-maturing seasonal cultivars with better yields (Pfadenhauer 2020 ; Bonacci 2019 ). Extending the planting season may allow additional crops each year; when warming leads to frequent warmer months highs over critical thresholds, a split season with a brief summer fallow may be conceivable for short-period crops such as wheat barley, cereals, and many other vegetable crops. The capacity to prolong the planting season in tropical and subtropical places where the harvest season is constrained by precipitation or agriculture farming occurs after the year may be more limited and dependent on how precipitation patterns vary (Wu et al. 2017 ).

The genetic component is comprehensive for many yields, but it is restricted like kiwi fruit for a few. Ali et al. ( 2017 ) investigated how new crops will react to climatic changes (also stated in Mall et al. 2017 ). Hot temperature, drought, insect resistance; salt tolerance; and overall crop production and product quality increases would all be advantageous (Akkari 2016 ). Genetic mapping and engineering can introduce a greater spectrum of features. The adoption of genetically altered cultivars has been slowed, particularly in the early forecasts owing to the complexity in ensuring features are expediently expressed throughout the entire plant, customer concerns, economic profitability, and regulatory impediments (Wirehn 2018 ; Davidson et al. 2016 ).

To get the full benefit of the CO 2 would certainly require additional nitrogen and other fertilizers. Nitrogen not consumed by the plants may be excreted into groundwater, discharged into water surface, or emitted from the land, soil nitrous oxide when large doses of fertilizer are sprayed. Increased nitrogen levels in groundwater sources have been related to human chronic illnesses and impact marine ecosystems. Cultivation, grain drying, and other field activities have all been examined in depth in the studies (Barua et al. 2018 ).

  • The technological and socio-economic adaptation

The policy consequence of the causative conclusion is that as a source of alternative energy, biofuel production is one of the routes that explain oil price volatility separate from international macroeconomic factors. Even though biofuel production has just begun in a few sample nations, there is still a tremendous worldwide need for feedstock to satisfy industrial expansion in China and the USA, which explains the food price relationship to the global oil price. Essentially, oil-exporting countries may create incentives in their economies to increase food production. It may accomplish by giving farmers financing, seedlings, fertilizers, and farming equipment. Because of the declining global oil price and, as a result, their earnings from oil export, oil-producing nations may be unable to subsidize food imports even in the near term. As a result, these countries can boost the agricultural value chain for export. It may be accomplished through R&D and adding value to their food products to increase income by correcting exchange rate misalignment and adverse trade terms. These nations may also diversify their economies away from oil, as dependence on oil exports alone is no longer economically viable given the extreme volatility of global oil prices. Finally, resource-rich and oil-exporting countries can convert to non-food renewable energy sources such as solar, hydro, coal, wind, wave, and tidal energy. By doing so, both world food and oil supplies would be maintained rather than harmed.

IRENA’s modeling work shows that, if a comprehensive policy framework is in place, efforts toward decarbonizing the energy future will benefit economic activity, jobs (outweighing losses in the fossil fuel industry), and welfare. Countries with weak domestic supply chains and a large reliance on fossil fuel income, in particular, must undertake structural reforms to capitalize on the opportunities inherent in the energy transition. Governments continue to give major policy assistance to extract fossil fuels, including tax incentives, financing, direct infrastructure expenditures, exemptions from environmental regulations, and other measures. The majority of major oil and gas producing countries intend to increase output. Some countries intend to cut coal output, while others plan to maintain or expand it. While some nations are beginning to explore and execute policies aimed at a just and equitable transition away from fossil fuel production, these efforts have yet to impact major producing countries’ plans and goals. Verifiable and comparable data on fossil fuel output and assistance from governments and industries are critical to closing the production gap. Governments could increase openness by declaring their production intentions in their climate obligations under the Paris Agreement.

It is firmly believed that achieving the Paris Agreement commitments is doubtlful without undergoing renewable energy transition across the globe (Murshed 2020 ; Zhao et al. 2022 ). Policy instruments play the most important role in determining the degree of investment in renewable energy technology. This study examines the efficacy of various policy strategies in the renewable energy industry of multiple nations. Although its impact is more visible in established renewable energy markets, a renewable portfolio standard is also a useful policy instrument. The cost of producing renewable energy is still greater than other traditional energy sources. Furthermore, government incentives in the R&D sector can foster innovation in this field, resulting in cost reductions in the renewable energy industry. These nations may export their technologies and share their policy experiences by forming networks among their renewable energy-focused organizations. All policy measures aim to reduce production costs while increasing the proportion of renewables to a country’s energy system. Meanwhile, long-term contracts with renewable energy providers, government commitment and control, and the establishment of long-term goals can assist developing nations in deploying renewable energy technology in their energy sector.

Author contribution

KA: Writing the original manuscript, data collection, data analysis, Study design, Formal analysis, Visualization, Revised draft, Writing-review, and editing. MZQ: Writing the original manuscript, data collection, data analysis, Writing-review, and editing. HS: Contribution to the contextualization of the theme, Conceptualization, Validation, Supervision, literature review, Revised drapt, and writing review and editing. MM: Writing review and editing, compiling the literature review, language editing. HM: Writing review and editing, compiling the literature review, language editing. IY: Contribution to the contextualization of the theme, literature review, and writing review and editing.

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The authors declare no competing interests.

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Kashif Abbass, Email: nc.ude.tsujn@ssabbafihsak .

Muhammad Zeeshan Qasim, Email: moc.kooltuo@888misaqnahseez .

Huaming Song, Email: nc.ude.tsujn@gnimauh .

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The Global Climate Plan: A Global Plan to End Climate Change and Extreme Poverty

143 Pages Posted: 4 Jun 2024

Adrien Fabre

CNRS; CIRED, International Research Center on Environment & Development, France

Date Written: June 01, 2024

Gabriel Zucman: "This book is essential reading for all citizens committed to justice and progress. How can we move towards a fairer world while effectively combating climate change? Adrien Fabre proposes a clear and convincing solution that deserves to be widely debated. The surest instrument for tackling climate change is well known: it consists of capping greenhouse gas emissions through a system of tradable quotas, the quantities of which must gradually decrease to reach zero shortly after the middle of the 21st century. By redistributing the revenues generated equally among all human beings, this mechanism would make it possible to finance a global basic income of around €50 per month per person, thus eradicating the most extreme forms of poverty. Utopia? Since completing his thesis, Adrien Fabre has specialized in opinion surveys on climate and redistribution. And this is where his treatise becomes fascinating. For his meticulous work overturns a commonly accepted idea: that the inhabitants of rich countries are hostile to international redistribution. In reality," he writes, 'people are willing to embrace ecological change and solidarity - as long as the effort is international, equitably shared, and falls first on the richest.' " This promising result deserves to be championed, and that's what the Global Redistribution Advocates movement is all about. Everyone is invited to join the movement by signing its petition, by joining, or by spreading its message.

Keywords: climate policy, climate negotiations

Suggested Citation: Suggested Citation

Adrien Fabre (Contact Author)

Cired, international research center on environment & development, france ( email ).

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Uncertainty, Social Valuation, and Climate Change Policy

climate change and poverty research paper

Uncertainty, as it pertains to climate change and other policy challenges, oper-ates through multiple channels. Such challenges are commonly framed using social valuations such as the social cost of climate change and the social value of research and development. These valuations have contributions that vary across horizons. We propose decompositions when the nature of this uncertainty is broadly conceived. By drawing on insights from decision theory, stochastic impulse response theory, and the pricing of uncertain cash flows, we provide novel characterizations. We use these methods to illustrate when and why uncertainty leads to more proactive policy approaches to climate change.

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Asset pricing under smooth ambiguity in continuous time, the price of macroeconomic uncertainty with tenuous beliefs, the distributional effects of student loan forgiveness.

Working papers, bank competition and strategic adaptation to climate change.

By Dasol Kim, Luke M. Olson, and Toan Phan

Published: June 21, 2024

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A map of the continental U.S. showing average flood risk at the zip code-level as calculated by First Street Foundation. Areas of higher flood risk are widely distributed across the country with some of the riskiest zip codes located near bodies of water.

Banks face an array of risks when lending to clients, some of which are not well known when a bank originates a loan. When a new risk emerges or a risk’s importance increases, banks mitigate these risks by adjusting their lending behavior (Working Paper 24-03).

How does competition affect banks’ adaptation to emergent risks for which there is limited supervisory oversight? The analysis matches detailed supervisory data on home equity lines of credit with high resolution flood projections to identify climate risks. Following Hurricane Harvey, banks updated their internal risk models to better reflect flood risk projections, even in areas unaffected by the disaster. These updates are only detected in banks with exposures to the disaster, indicating heterogeneous bank learning. We use this heterogeneity to identify how bank adaptation is affected by competition. Exposed banks reduce lending to areas with higher flood risks, but only in less competitive markets, suggesting that competition fosters risk-taking over risk mitigation. Additionally, banks are less likely to adapt in markets where competitors are also less likely to do so, suggesting a strategic complementarity in bank adaptation. More broadly, our paper sheds light on the role of competitive forces in how banks manage emerging risks and relevant supervisory challenges.

Keywords: Banks, climate risk, real estate, natural disasters, competition, moral hazard JEL Classifications: D14, E6, G21, Q54

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Global Growth Is Stabilizing for the First Time in Three Years

But 80% of world population will experience slower growth than in pre-COVID decade

WASHINGTON, June 11, 2024 — The global economy is expected to stabilize for the first time in three years in 2024—but at a level that is weak by recent historical standards, according to the World Bank’s latest Global Economic Prospects report.

Global growth is projected to hold steady at 2.6% in 2024 before edging up to an average of 2.7% in 2025-26. That is well below the 3.1% average in the decade before COVID-19. The forecast implies that over the course of 2024-26 countries that collectively account for more than 80% of the world’s population and global GDP would still be growing more slowly than they did in the decade before COVID-19.

Overall, developing economies are projected to grow 4% on average over 2024-25, slightly slower than in 2023. Growth in low-income economies is expected to accelerate to 5% in 2024 from 3.8% in 2023. However, the forecasts for 2024 growth reflect downgrades in three out of every four low-income economies since January. In advanced economies, growth is set to remain steady at 1.5% in 2024 before rising to 1.7% in 2025.

“Four years after the upheavals caused by the pandemic, conflicts, inflation, and monetary tightening, it appears that global economic growth is steadying,” said Indermit Gill, the World Bank Group’s Chief Economist and Senior Vice President. “ However, growth is at lower levels than before 2020. Prospects for the world’s poorest economies are even more worrisome. They face punishing levels of debt service, constricting trade possibilities, and costly climate events. Developing economies will have to find ways to encourage private investment, reduce public debt, and improve education, health, and basic infrastructure. The poorest among them—especially the 75 countries eligible for concessional assistance from the International Development Association—will not be able to do this without international support.”

This year, one in four developing economies is expected to remain poorer than it was on the eve of the pandemic in 2019. This proportion is twice as high for countries in fragile- and conflict-affected situations. Moreover, the income gap between developing economies and advanced economies is set to widen in nearly half of developing economies over 2020-24 —the highest share since the 1990s. Per capita income in these economies—an important indicator of living standards—is expected to grow by 3.0% on average through 2026, well below the average of 3.8% in the decade before COVID-19.

Global inflation is expected to moderate to 3.5% in 2024 and 2.9% in 2025, but the pace of decline is slower than was projected just six months ago. Many central banks, as a result, are expected to remain cautious in lowering policy interest rates. Global interest rates are likely to remain high by the standards of recent decades—averaging about 4% over 2025-26, roughly double the 2000-19 average.

“Although food and energy prices have moderated across the world, core inflation remains relatively high—and could stay that way,” said Ayhan Kose, the World Bank’s Deputy Chief Economist and Director of the Prospects Group . “That could prompt central banks in major advanced economies to delay interest-rate cuts. An environment of ‘higher-for-longer’ rates would mean tighter global financial conditions and much weaker growth in developing economies.”

The latest Global Economic Prospects report also features two analytical chapters of topical importance. The first outlines how public investment can be used to accelerate private investment and promote economic growth. It finds that public investment growth in developing economies has halved since the global financial crisis, dropping to an annual average of 5% in the past decade. Yet public investment can be a powerful policy lever. For developing economies with ample fiscal space and efficient government spending practices, scaling up public investment by 1% of GDP can increase the level of output by up to 1.6% over the medium term.

The second analytical chapter explores why small states—those with a population of around 1.5 million or less—suffer chronic fiscal difficulties. Two-fifths of the 35 developing economies that are small states are at high risk of debt distress or already in it. That’s roughly twice the share for other developing economies. Comprehensive reforms are needed to address the fiscal challenges of small states. Revenues could be drawn from a more stable and secure tax base. Spending efficiency could be improved —especially in health, education, and infrastructure. Fiscal frameworks could be adopted to manage the higher frequency of natural disasters and other shocks. Targeted and coordinated global policies can also help put these countries on a more sustainable fiscal path.

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Regional Outlooks:

East Asia and Pacific:  Growth is expected to decelerate to 4.8% in 2024 and to 4.2% in 2025. For more, see  regional overview.

Europe and Central Asia:  Growth is expected to edge down to 3.0% in 2024 before moderating to 2.9% in 2025. For more, see  regional overview .

Latin America and the Caribbean:  Growth is expected to decline to 1.8% in 2024 before picking up to 2.7% in 2025. For more, see  regional overview .

Middle East and North Africa:  Growth is expected to pick up to 2.8% in 2024 and 4.2% in 2025. For more, see  regional overview.

South Asia:  Growth is expected to slow to 6.2% in 2024 and remain steady at 6.2% in 2025. For more, see regional overview.

Sub-Saharan Africa: Growth is expected to pick up to 3.5% in 2024 and to 3.9% in 2025. For more, see  regional overview.



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