Economic analysis of climate change

An economic analysis of climate change uses economic tools and models to calculate the magnitude and distribution of damages caused by climate change. It can also give guidance for the best policies for mitigation and adaptation to climate change from an economic perspective. There are many economic models and frameworks. For example, in a cost–benefit analysis, the trade offs between climate change impacts, adaptation, and mitigation are made explicit. For this kind of analysis, integrated assessment models (IAMs) are useful. Those models link main features of society and economy with the biosphere and atmosphere into one modelling framework. The total economic impacts from climate change are difficult to estimate. In general, they increase the more the global surface temperature increases (see climate change scenarios).

Estimated median income loss or gain per person by 2050 due to climate change, compared to a scenario with no climate impacts (red colour indicates a loss, blue colour a gain).

Many effects of climate change are linked to market transactions and therefore directly affect metrics like GDP or inflation. However, there are also non-market impacts which are harder to translate into economic costs. These include the impacts of climate change on human health, biomes and ecosystem services. Economic analysis of climate change is challenging as climate change is a long-term problem. Furthermore, there is still a lot of uncertainty about the exact impacts of climate change and the associated damages to be expected. Future policy responses and socioeconomic development are also uncertain.

Economic analysis also looks at the economics of climate change mitigation and the cost of climate adaptation. Mitigation costs will vary according to how and when emissions are cut. Early, well-planned action will minimize the costs. Globally, the benefits of keeping warming under 2 °C exceed the costs. Cost estimates for mitigation for specific regions depend on the quantity of emissions allowed for that region in future, as well as the timing of interventions. Economists estimate the cost of climate change mitigation at between 1% and 2% of GDP. The costs of planning, preparing for, facilitating and implementing adaptation are also difficult to estimate, depending on different factors. Across all developing countries, they have been estimated to be about USD 215 billion per year up to 2030, and are expected to be higher in the following years.

Purposes

Economic analysis of climate change is an umbrella term for a range of investigations into the economic costs around the effects of climate change, and for preventing or softening those effects. These investigations can serve any of the following purposes:

• estimating the potential global aggregate economic costs of climate change (i.e. global climate damages)
• estimating sectoral or regional economic costs of climate change (e.g. costs to agriculture sector or energy services)
• estimating economic costs of facilitating and implementing climate change mitigation and adaptation strategies (varying with the objectives and the levels of action required); see also economics of climate change mitigation.
• monetising the projected impacts to society per additional metric tonne of carbon emissions (social cost of carbon)
• informing decisions about global climate management strategy (through UN institutions) or policy decisions in some countries

The economic impacts of climate change also include any mitigation (for example, limiting the global average temperature below 2 °C) or adaption (for example, building flood defences) employed by nations or groups of nations, which might infer economic consequences. They also take into account that some regions or sectors benefit from low levels of warming, for example through lower energy demand or agricultural advantages in some markets.

There are wider policy (and policy coherence) considerations of interest. For example, in some areas, policies designed to mitigate climate change may contribute positively towards other sustainable development objectives, such as abolishing fossil fuel subsidies which would reduce air pollution and thus save lives. Direct global fossil fuel subsidies reached $319 billion in 2017, and $5.2 trillion when indirect costs such as air pollution are priced in. In other areas, the cost of climate change mitigation may divert resources away from other socially and environmentally beneficial investments (the opportunity costs of climate change policy).

Types of economic models

Various economic tools are employed to understand the economic aspects around impacts of climate change, climate change mitigation and adaptation. Several sets of tools or approaches exist. Econometric models (statistical models) are used to integrate the broad impacts of climate change with other economic drivers, to quantify the economic costs and assess the value of climate-related policies, often for a specific sector or region. Structural economic models look at market and non-market impacts affecting the whole economy through its inputs and outputs. Process models simulate physical, chemical and biological processes under climate change, and the economic effects.

Process-based models

This section is an excerpt from Integrated assessment modelling § Process-based models.[edit]

Annual greenhouse gas emissions in the various NGFS climate scenarios 2022, based on the REMIND-MAgPIE model by the Potsdam Institute for Climate Impact Research

Intergovernmental Panel on Climate Change (IPCC) has relied on process-based integrated assessment models to quantify mitigation scenarios. They have been used to explore different pathways for staying within climate policy targets such as the 1.5 °C target agreed upon in the Paris Agreement. Moreover, these models have underpinned research including energy policy assessment and simulate the Shared socioeconomic pathways. Notable modelling frameworks include IMAGE, MESSAGEix, AIM/GCE, GCAM, REMIND-MAgPIE, and WITCH-GLOBIOM. While these scenarios are highly policy-relevant, interpretation of the scenarios should be done with care.

Non-equilibrium models include those based on econometric equations and evolutionary economics (such as E3ME), and agent-based models (such as the agent-based DSK-model). These models typically do not assume rational and representative agents, nor market equilibrium in the long term.

Structural models

Computable general equilibrium models

This section is an excerpt from Computable general equilibrium.[edit]

Computable general equilibrium (CGE) models are a class of economic models that use actual economic data to estimate how an economy might react to changes in policy, technology or other external factors. CGE models are also referred to as AGE (applied general equilibrium) models. A CGE model consists of equations describing model variables and a database (usually very detailed) consistent with these model equations. The equations tend to be neoclassical in spirit, often assuming cost-minimizing behaviour by producers, average-cost pricing, and household demands based on optimizing behaviour.

CGE models are useful whenever we wish to estimate the effect of changes in one part of the economy upon the rest. They have been used widely to analyse trade policy. More recently, CGE has been a popular way to estimate the economic effects of measures to reduce greenhouse gas emissions.

Aggregate cost-benefit models

See also: Integrated assessment modelling § Aggregate cost-benefit models

Integrated assessment models (IAMs) are also used make aggregate estimates of the costs of climate change. These (cost-benefit) models balance the economic implications of mitigation and climate damages to identify the pathway of emissions reductions that will maximize total economic welfare. In other words, the trade-offs between climate change impacts, adaptation, and mitigation are made explicit. The costs of each policy and the outcomes modelled are converted into monetary estimates.

The models incorporate aspects of the natural, social, and economic sciences in a highly aggregated way. Compared to other climate-economy models (including process-based IAMs), they do not have the structural detail necessary to model interactions with energy systems, land-use etc. and their economic implications.

Statistical (econometric) methods

See also: Econometric model

A more recent modelling approach uses empirical, statistical methods to investigate how the economy is affected by weather variation. This approach can causatively identify effects of temperature, rainfall and other climate variables on agriculture, energy demand, industry and other economic activity. Panel data are used giving weather variation over time and spatial areas, eg. ground station observations or (interpolated) gridded data. These are typically aggregated for economic analysis eg. to investigate effects on national economies. These studies examine temperature and rainfall, and events such as droughts and windstorms. They show that for example, hot years are linked to lower income growth in poor countries, and low rainfall is linked to reduced incomes in Africa. Other econometric studies show that there are negative impacts of hotter temperatures on agricultural output, and on labour productivity in factories, call centres and in outdoor industries such as mining and forestry. The analyses are used to estimate the costs of climate change in the future.

Analytical frameworks

Cost–benefit analysis

See also: Integrated assessment modelling § Aggregate cost-benefit models

Standard cost–benefit analysis (CBA) has been applied to the problem of climate change. In a CBA framework, the negative and positive impacts associated with a given action are converted into monetary estimates. This is also referred to as a monetized cost–benefit framework. Various types of model can provide information for CBA, including energy-economy-environment models (process models) that study energy systems and their transitions. Some of these models may include a physical model of the climate. Computable General Equilibrium (CGE) structural models investigate effects of policies (including climate policies) on economic growth, trade, employment, and public revenues. However, most CBA analyses are produced using aggregate integrated assessment models. These aggregate-type IAMs are particularly designed for doing CBA of climate change.

The CBA framework requires (1) the valuation of costs and benefits using willingness to pay (WTP) or willingness to accept (WTA) compensation as a measure of value, and (2) a criterion for accepting or rejecting proposals:

For (1), in CBA where WTP/WTA is used, climate change impacts are aggregated into a monetary value, with environmental impacts converted into consumption equivalents, and risk accounted for using certainty equivalents. Values over time are then discounted to produce their equivalent present values. The valuation of costs and benefits of climate change can be controversial because some climate change impacts are difficult to assign a value to, e.g., ecosystems and human health.

For (2), the standard criterion is the Kaldor–Hicks compensation principle. According to the compensation principle, so long as those benefiting from a particular project compensate the losers, and there is still something left over, then the result is an unambiguous gain in welfare. If there are no mechanisms allowing compensation to be paid, then it is necessary to assign weights to particular individuals. One of the mechanisms for compensation is impossible for this problem: mitigation might benefit future generations at the expense of current generations, but there is no way that future generations can compensate current generations for the costs of mitigation. On the other hand, should future generations bear most of the costs of climate change, compensation to them would not be possible.

CBA has several strengths: it offers an internally consistent and global comprehensive analysis of impacts. Furthermore, sensitivity analysis allows critical assumptions in CBA analysis to be changed. This can identify areas where the value of information is highest and where additional research might have the highest payoffs. However, there are many uncertainties that affect cost–benefit analysis, for example, sector- and country-specific damage functions.

Damage functions

This graph shows estimation confidence intervals from a meta-analysis of researchers as well as by the Stern Review in 2006 (damage costs measured as percent GDP).

Damage functions play an important role in estimating the costs associated with potential damages caused by climate-related hazards. They quantify the relationship between the intensity of the hazard, other factors such as the vulnerability of the system, and the resulting damages. For example, damage functions have been developed for sea level rise, agricultural productivity, or heat effects on labour productivity. In a CBA framework, damages are monetized to facilitate comparison with the benefits of proposed actions or policies. Sensitivity analysis is conducted to assess the robustness of the results to changes in assumptions and parameters, including those of the damage function.

Cost-effectiveness analysis

See also: Economics of climate change mitigation § Decision analysis

Cost-Effectiveness Analysis (CEA) is preferable to CBA when the benefits of impacts, adaptation and mitigation are difficult to estimate in monetary terms. A CEA can be used to compare different policy options for achieving a well-defined goal. This goal (i.e. the benefit) is usually expressed as the amount of GHG emissions reduction in the analysis of mitigation measures. For adaptation measures, there is no single common goal or metric for the economic benefits. Adaptation involves responding to different types of risks in different sectors and local contexts. For example, the goal might be the reduction of land area in hectares at risk to sea level rise.

CEA involves the costing of each option, and providing a cost per unit of effectiveness. For example, cost per tonne of GHG reduced ($/tCO2). This allows the ranking of policy options. This ranking can help decision-maker to understand which are the most cost-effective options, i.e. those that deliver high benefits for low costs. CEA can be used for minimising net costs for achieving pre-defined policy targets, such as meeting an emissions reduction target for a given sector.

CEA, like CBA, is a type of decision analysis method. Many of these methods work well when different stakeholders work together on a problem to understand and manage risks. For example, by discussing how well certain options might work in the real world. Or by helping in measuring the costs and benefits as part of a CEA.

Some authors have focused on a disaggregated analysis of climate change impacts. "Disaggregated" refers to the choice to assess impacts in a variety of indicators or units, e.g., changes in agricultural yields and loss of biodiversity. By contrast, monetized CBA converts all impacts into a common unit (money), which is used to assess changes in social welfare.

Scaling the effect of wealth to the national level: richer (developed) countries emit more CO₂ per person than poorer (developing) countries. Emissions are roughly proportional to GDP per person, though the rate of increase diminishes with average GDPs/pp of about $10,000.

Scenario-based assessments

Main article: Climate change scenario

The long time scales and uncertainty associated with global warming have led analysts to develop "scenarios" of future environmental, social and economic changes. These scenarios can help governments understand the potential consequences of their decisions.

The projected temperature in climate change scenarios is subject to scientific uncertainty (e.g., the relationship between concentrations of GHGs and global mean temperature, which is called the climate sensitivity). Projections of future atmospheric concentrations based on emission pathways are also affected by scientific uncertainties, e.g., over how carbon sinks, such as forests, will be affected by future climate change.

One of the economic aspects of climate change is producing scenarios of future economic development. Future economic developments can, for example, affect how vulnerable society is to future climate change, what the future impacts of climate change might be, as well as the level of future GHG emissions.

Scenarios are neither "predictions" nor "forecasts" but are stories of possible futures that provide alternate outcomes relevant to a decision-maker or other user. These alternatives usually also include a "baseline" or reference scenario for comparison. "Business-as-usual" scenarios have been developed in which there are no additional policies beyond those currently in place, and socio-economic development is consistent with recent trends. This term is now used less frequently than in the past.

In scenario analysis, scenarios are developed that are based on differing assumptions of future development patterns. An example of this are the shared socioeconomic pathways produced by the Intergovernmental Panel on Climate Change (IPCC). These project a wide range of possible future emissions levels.

Scenarios often support sector-specific analysis of the physical effects and economic costs of climate change. Scenarios are used with cost–benefit analysis or cost-effectiveness analysis of climate policies.

Risk management

Main article: Climate risk

Risk management can be used to evaluate policy decisions based a range of criteria or viewpoints, and is not restricted to the results of particular type of analysis, e.g., monetized CBA. Another approach is that of uncertainty analysis, where analysts attempt to estimate the probability of future changes in emission levels.

In a cost–benefit analysis, an acceptable risk means that the benefits of a climate policy outweigh the costs of the policy. The standard rule used by public and private decision makers is that a risk will be acceptable if the expected net present value is positive. The expected value is the mean of the distribution of expected outcomes. In other words, it is the average expected outcome for a particular decision. This criterion has been justified on the basis that:

• a policy's benefits and costs have known probabilities
• economic agents (people and organizations) can diversify their own risk through insurance and other markets.

On the second point, it has been suggested that insurance could be bought against climate change risks. Policymakers and investors are beginning to recognize the implications of climate change for the financial sector, from both physical risks (damage to property, infrastructure, and land) and transition risk due to changes in policy, technology, and consumer and market behavior. Financial institutions are becoming increasingly aware of the need to incorporate the economics of low carbon emissions into business models.

In the scientific literature, there is sometimes a focus on "best estimate" or "likely" values of climate sensitivity. However, from a risk management perspective, values outside of "likely" ranges are relevant, because, though these values are less probable, they could be associated with more severe climate impacts (the statistical definition of risk = probability of an impact × magnitude of the impact).

Analysts have also looked at how uncertainty over climate sensitivity affects economic estimates of climate change impacts. Policy guidance from cost-benefit analysis (CBA) can be extremely divergent depending on the assumptions employed. Hassler et al use integrated assessment modeling to examine a range of estimates and what happens at extremes.

Iterative risk management

Two related ways of thinking about the problem of climate change decision-making in the presence of uncertainty are iterative risk management and sequential decision making. Considerations in a risk-based approach might include, for example, the potential for low-probability, worst-case climate change impacts. One of the responses to the uncertainties of global warming is to adopt a strategy of sequential decision making. Sequential decision making refers to the process in which the decision maker makes consecutive observations of the process before making a final decision. This strategy recognizes that decisions on global warming need to be made with incomplete information, and that decisions in the near term will have potentially long-term impacts. Governments may use risk management as part of their policy response to global warming.

An approach based on sequential decision making recognizes that, over time, decisions related to climate change can be revised in the light of improved information. This is particularly important with respect to climate change, due to the long-term nature of the problem. A near-term hedging strategy concerned with reducing future climate impacts might favor stringent, near-term emissions reductions. As stated earlier, carbon dioxide accumulates in the atmosphere, and to stabilize the atmospheric concentration of CO₂, emissions would need to be drastically reduced from their present level. Stringent near-term emissions reductions allow for greater future flexibility with regard to a low stabilization target, e.g., 450 parts per million (ppm) CO₂. To put it differently, stringent near-term emissions abatement can be seen as having an option value in allowing for lower, long-term stabilization targets. This option may be lost if near-term emissions abatement is less stringent.

On the other hand, a view may be taken that points to the benefits of improved information over time. This may suggest an approach where near-term emissions abatement is more modest. Another way of viewing the problem is to look at the potential irreversibility of future climate change impacts (e.g., damages to biomes and ecosystems) against the irreversibility of making investments in efforts to reduce emissions.

Portfolio analysis

An example of a framework that is based on risk management is portfolio analysis. This approach is based on portfolio theory, originally applied in the areas of finance and investment. It has also been applied to the analysis of climate change. The idea is that a reasonable response to uncertainty is to invest in a wide portfolio of options. More specifically, the aim is to minimise the variance and co-variance of the performance of investments in the portfolio. In the case of climate change mitigation, performance is measured by how much GHG emissions reduction is achieved. On the other hand, climate change adaptation acts as insurance against the chance that unfavourable impacts occur. The performance of adaptation options could either be defined in economic terms, e.g. revenue, or as physical metrics, e.g. the quantity of water conserved.

It is important to compare alternative portfolios of options across different future climate change scenarios in order to take into account uncertainty in climate impacts, GHG emission trends etc. The options should ideally be diversified to be effective in different scenarios: i.e. some options suited for a no/low climate change scenario, with other options being suited for scenarios with severe climate changes.

Investment and financial flows

Investment and financial flow (I&FF) studies typically consider how much it might cost to increase the resilience of future investments or financial flows. They also investigate the potential sources of investment funds and the types of financing entities or actors. Aggregated studies assess the sensitivity of future investments, estimating the risk from climate change and estimating the additional investment needed to increase resilience. More detailed studies undertake investment and financial flow analysis at a sectoral level to provide detailed costing of the additional marginal costs needed for building resilience.

Costs of impacts of climate change

At the global level (aggregate costs)

After 2050, the global impacts of the high-emission scenario on economic output estimated to exceed those of the low-emission scenario at 1% statistical significance

There is a growing number of weather-related disasters in the United States costing above one billion dollars

Global aggregate costs (also known as global damages or losses) sum up the predicted impacts of climate change across all market sectors (e.g. including costs to agriculture, energy services and tourism) and can also include non-market impacts (e.g. on ecosystems and human health) for which it is possible to assign monetary values. A study in 2024 projected that by 2050, climate change will reduce average global incomes by likely 19% (confidence interval 11-29%), relative to a counterfactual where no climate change occurs. The global economy and per capita income would still grow relative to present, but the global annual damages would reach about $38 trillion (in 2005 International dollars) by 2050, and increase a lot further under high emissions. In comparison, limiting global warming to 2 °C would by 2050 cost about $6 trillion per year, or far less than the anticipated annual damages, emphasizing the economic benefits of proactive climate mitigation.

Another study, which checked the data from the last 120 years, found that climate change has already reduced welfare by 29% and further temperature rise will bring this number to 47%. The temperature rise during the years 1960-2019 alone has already cut current GDP per capita by 18%. A rise by 1 degree in global temperature reduces global GDP by 12%. An increase of 3 degrees by 2100, will reduce capital by 50%. The effects are like experiencing the 1929 Great Depression permanently. The appropriate social cost of carbon is 1065 dollars per tonne of CO2.

Global estimates are often based on an aggregation of independent sector and/or regional studies and results, with complex interactions modelled. For example, there is uncertainty in how physical and natural systems may respond to climate change. Potential socioeconomic changes, including how human societies might mitigate and adapt to climate change also need consideration. The uncertainty and complexities associated with climate change and have led analysts to develop "scenarios" with which they can explore different possibilities.

Global economic losses due to extreme weather, climate and water events are increasing. Costs have increased sevenfold from the 1970s to the 2010s. Direct losses from disasters have averaged above US$330 billion annually between 2015 and 2021. Climate change has contributed to the increased probability and magnitude of extreme events. When a vulnerable community is exposed to extreme climate or weather events, disasters can occur. Socio-economic factors have contributed to the observed trend of global disaster losses, such as population growth and increased wealth. This shows that increased exposure is the most important driver of losses. However, part of these are also due to human-induced climate change. Extreme Event Attribution quantifies how climate change is altering the probability and magnitude of extreme events. On a case-by-case basis, it is feasible to estimate how the magnitude and/or probability of the extreme event has shifted due to climate change. These attributable changes have been identified for many individual extreme heat events and rainfall events. Using all available data on attributable changes, one study estimated the global losses to average US$143 billion per year between 2000 and 2019. This includes a statistical loss of life value of 90 billion and economic damages of 53 billion per year.

Estimates of the economic impacts from climate change in future years are most often measured as percent global GDP change, relative to GDP without additional climate change. The 2022 IPCC report compared the latest estimates of many modelling and meta-analysis studies. It found wide variety in the results. These vary depending on the assumptions used in the IPCC socioeconomic scenarios. The same set of scenarios are used in all of the climate models.

Estimates are found to increase non-linearly with global average temperature change. Global temperature change projection ranges (corresponding to each cost estimate) are based on IPCC assessment on the physical science in the same report. It finds that with high warming (~4 °C) and low adaptation, annual global GDP might be reduced by 10–23% by 2100 because of climate change. The same assessment finds smaller GDP changes with reductions of 1–8%, assuming assuming low warming, more adaptation, and using different models. These global economic cost estimates do not take into account impacts on social well-being or welfare or distributional effects. Nor do they fully consider climate change adaptation responses.

One 2020 study estimated economic losses due to climate change could be between 127 and 616 trillion dollars extra until 2100 with current commitments, compared to 1.5 °C or well below 2 °C compatible action. Failure to implement current commitments raises economic losses to 150–792 trillion dollars until 2100.

Economic impacts also include inflation from rising insurance premiums, energy costs and food prices.

High emissions scenarios

The total economic impacts from climate change increase for higher temperature changes. For instance, total damages are estimated to be 90% less if global warming is limited to 1.5 °C compared to 3.66 °C, a warming level chosen to represent no mitigation. In an Oxford Economics study high emission scenario, a temperature rise of 2 degrees by the year 2050 would reduce global GDP by 2.5–7.5%. By the year 2100 in this case, the temperature would rise by 4 degrees, which could reduce the global GDP by 30% in the worst case.

One 2018 study found that potential global economic gains if countries implement mitigation strategies to comply with the 2 °C target set at the Paris Agreement are in the vicinity of US$17 trillion per year up to 2100, compared to a very high emission scenario.

Underestimation of economic impacts

The amount by which greenhouse gas emissions are reduced is forecast to substantially affect the number of Winter Olympic Game venues that will have reliably cold conditions.

Studies in 2019 suggested that economic damages due to climate change have been underestimated, and may be severe, with the probability of disastrous tail-risk events.

Tipping points are critical thresholds that, when crossed, lead to large, accelerating and often irreversible changes in the climate system. The science of tipping points is complex and there is great uncertainty as to how they might unfold. Economic analyses often exclude the potential effect of tipping points. A 2018 study noted that the global economic impact is underestimated by a factor of two to eight, when tipping points are excluded from consideration.

The Stern Review from 2006 for the British Government predicted that world GDP would be reduced by several percent due to climate related costs. However, their calculations may omit ecological effects that are difficult to quantify economically (such as human deaths or loss of biodiversity) or whose economic consequences will manifest slowly. Therefore, their calculations may be an underestimate. The study has received both criticism and support from other economists.

By region

Countries with the lowest GDPs per capita (yellow) and the lowest cumulative emissions will often suffer the greatest declines in their income relative to a hypothetical future where the impacts of climate change were not happening

Other studies investigate economic losses by GDP change per country or by per country per capita. Findings show large differences among countries and within countries. The estimated GDP changes in some developing countries are similar to some of the worst country-level losses during historical economic recessions. Economic losses are risks to living standards, which are more likely to be severe in developing countries. Climate change can push more people into extreme poverty or keep people poor, especially through particularly climate-sensitive sectors such as agriculture and fisheries. Climate change may also increase income inequality within countries as well as between them, particularly affecting low-income groups.

The economic impact of changes in annual mean temperature is estimated to be lower at higher latitudes despite higher temperature changes due to lower estimated economic vulnerability to temperature changes. Reduced daily temperature variability at high latitudes shows positive estimated economic impact, with opposite effects at lower latitudes and Europe. Economic effects due to changes in total annual precipitation show regional patterns generally opposite to changes in the number of wet days.

According to a study by reinsurance company Swiss Re in 2021 the economies of wealthy countries like the US would likely shrink by approximately 7%, while some developing nations would be devastated, losing around 20% or in some cases 40% of their economic output.

A United States government report in November 2018 raised the possibility of US GDP going down 10% as a result of the warming climate, including huge shifts in geography, demographics and technology.

Economic impacts differ by region, North Africa, Middle East, South, Southeast and East Asia show statistical significance, with no statistical difference for Central Asia/Russia

By sector

Main articles: Climate change in Africa, Climate change in Asia, Climate change in the Americas, and Climate change in Europe

The distribution of warming impacts from emitters has been unequal, with high-income, high-emitting countries benefitting while harming low-income, low-emitting countries.

A number of economic sectors will be affected by climate change, including the livestock, forestry, and fisheries industries. Other sectors sensitive to climate change include the energy, insurance, tourism and recreation industries.

Health and productivity

See also: Effects of climate change on human health

Among the health impacts that have been studied, aggregate costs of heat stress (through loss of work time) have been estimated, as have the costs of malnutrition. However, it is usual for studies to aggregate the number of 'years of life lost' adjusted for years living with disability to measure effects on health.

In 2019 the International Labour Organization published a report titled: "Working on a warmer planet: The impact of heat stress on labour productivity and decent work", in which it claims that even if the rise in temperature will be limited to 1.5 degree, by the year 2030, Climate Change will cause losses in productivity reaching 2.2% of all the working hours, every year. This is equivalent to 80 million full-time jobs, or 2,400 billion dollars. The sector expected to be most affected is agriculture, which is projected to account for 60% of this loss. The construction sector is also projected to be severely impacted and accounts for 19% of projected losses. Other sectors that are most at risk are environmental goods and services, refuse collection, emergency, repair work, transport, tourism, sports and some forms of industrial work.

It has been estimated that 3.5 million people die prematurely each year from air pollution from fossil fuels. The health benefits of meeting climate goals substantially outweigh the costs of action. The health benefits of phasing out fossil fuels measured in money (estimated by economists using the value of life for each country) are substantially more than the cost of achieving the 2 degree C goal of the Paris Agreement.

Agriculture

This section is an excerpt from Effects of climate change on agriculture § Labour and economic effects.[edit]

Climate change is expected to exacerbate heat stress over at the North China Plain, which is particularly vulnerable as widespread irrigation results in very moist air. There is a risk that agricultural labourers will be physically unable to work outdoors on hot summer days, particularly under the scenario of greatest emissions and warming.

As extreme weather events become more common and more intense, floods and droughts can destroy crops and eliminate food supply, while disrupting agricultural activities and rendering workers jobless. With more costs to the farmer, some will no longer find it financially feasible to farm: i.e. some farmers may choose to permanently leave drought-affected areas. Agriculture employs the majority of the population in most low-income countries and increased costs can result in worker layoffs or pay cuts. Other farmers will respond by raising their food prices; a cost that is directly passed on to the consumer and affects the affordability of food. Some farms do not sell their produce but instead feed a family or community; without that food, people will not have enough to eat. This results in decreased production, increased food prices, and potential starvation in parts of the world. The agriculture industry in India makes up 52% of their employment and the Canadian Prairies supply 51% of Canadian agriculture; any changes in the production of food crops from these areas could have profound effects on the economy.

Notably, one estimate suggests that a warming of 3 °C (5.4 °F) relative to late 20th century (i.e. closer to 4 °C (7.2 °F) when compared to preindustrial temperatures – a level associated with the SSP5-8.5 scenario) would cause labour capacity in Sub-Saharan Africa and Southeast Asia to decline by 30 to 50%, as the number of days when outdoor workers experience heat stress increases: up to 250 days the worst-affected parts of these two continents and of Central and South America. This could then increase crop prices by around 5%.

Similarly, North China Plain is also expected to be highly affected, in part due to the region's extensive irrigation networks resulting in unusually moist air. In scenarios without aggressive action to stop climate change, some heatwaves could become extreme enough to cause mass mortality in outdoor labourers, although they will remain relatively uncommon (up to around once per decade starting from 2l00 under the most extreme scenario).

Further, the role of climate change in undernutrition and micronutrient deficiencies can be calculated as the loss of "years of full health".One estimate presented in 2016 suggests that under the scenario of strong warming and low adaptation due to high global conflict and rivalry, such losses may take up 0.4% of the global GDP and 4% of the GDP in India and the South Asian region by the year 2100.

Industry

Carbon-intensive industries and investors are expected to experience a significant increase in stranded assets with a potential ripple affect throughout the world economy.

Impact on living costs

Food prices could rise by as much as 3% per year due to climate change impacts.

Climateflation is the contribution to inflation of added costs brought about as a result of climate change. Energy prices are one of the most common examples cited of climateflation.

Natural disasters fueled by climate change have increased housing costs through insurance and by exacerbating housing shortages when those events make homes unlivable.

Utility of aggregated assessment

Projected economic impacts of 2 degrees of global warming on Senegal

There are a number of benefits of using aggregated assessments to measure economic impacts of climate change. They allow impacts to be directly compared between different regions and times. Impacts can be compared with other environmental problems and also with the costs of avoiding those impacts. A problem of aggregated analyses is that they often reduce different types of impacts into a small number of indicators. It can be argued that some impacts are not well-suited to this, e.g., the monetization of mortality and loss of species diversity. On the other hand, where there are monetary costs of avoiding impacts, it may not be possible to avoid monetary valuation of those impacts.

Costs of climate change mitigation measures

Main article: Economics of climate change mitigation

Climate change mitigation consist of human actions to reduce greenhouse gas emissions or to enhance carbon sinks that absorb greenhouse gases from the atmosphere.

This section is an excerpt from Climate change mitigation § Costs and funding.[edit]

Several factors affect mitigation cost estimates. One is the baseline. This is a reference scenario that the alternative mitigation scenario is compared with. Others are the way costs are modelled, and assumptions about future government policy. Cost estimates for mitigation for specific regions depend on the quantity of emissions allowed for that region in future, as well as the timing of interventions.

Mitigation costs will vary according to how and when emissions are cut. Early, well-planned action will minimize the costs. Globally, the benefits of keeping warming under 2 °C exceed the costs, which according to The Economist are affordable.

Economists estimate the cost of climate change mitigation at between 1% and 2% of GDP. While this is a large sum, it is still far less than the subsidies governments provide to the ailing fossil fuel industry. The International Monetary Fund estimated this at more than $5 trillion per year.

Another estimate says that financial flows for climate mitigation and adaptation are going to be over $800 billion per year. These financial requirements are predicted to exceed $4 trillion per year by 2030.

Globally, limiting warming to 2 °C may result in higher economic benefits than economic costs. The economic repercussions of mitigation vary widely across regions and households, depending on policy design and level of international cooperation. Delayed global cooperation increases policy costs across regions, especially in those that are relatively carbon intensive at present. Pathways with uniform carbon values show higher mitigation costs in more carbon-intensive regions, in fossil-fuels exporting regions and in poorer regions. Aggregate quantifications expressed in GDP or monetary terms undervalue the economic effects on households in poorer countries. The actual effects on welfare and well-being are comparatively larger.

Cost–benefit analysis may be unsuitable for analysing climate change mitigation as a whole. But it is still useful for analysing the difference between a 1.5 °C target and 2 °C. One way of estimating the cost of reducing emissions is by considering the likely costs of potential technological and output changes. Policymakers can compare the marginal abatement costs of different methods to assess the cost and amount of possible abatement over time. The marginal abatement costs of the various measures will differ by country, by sector, and over time.

Eco-tariffs on only imports contribute to reduced global export competitiveness and to deindustrialization.

Costs of climate change adaptation measures

This section is an excerpt from Climate finance § Adaptation costs and adaptation financing needs.[edit]

Adaptation costs are the costs of planning, preparing for, facilitating and implementing adaptation. Adaptation benefits can be estimated in terms of reduced damages from the effects of climate change. In economic terms, the cost to benefit ratio of adaptation shows that each dollar can deliver large benefits. For example, it is estimated that every US$1 billion invested in adaptation against coastal flooding leads to a US$14 billion reduction in economic damages. Investing in more resilient infrastructure in developing countries would provide an average of $4 in benefit for each $1 invested. In other words, a small percentage increase in investment costs can mitigate the potentially very large disruption to infrastructure costs.

A 2023 study found the overall adaptation costs for all developing countries to be around US$215 billion per year for the period up to 2030. The highest adaptation expenses are for river flood protection, infrastructure and coastal protection. They also found that in most cases, adaptation costs will be significantly higher by 2050.

It is difficult to estimate both the costs of adaptation and the adaptation finance needs. The costs of adaptation varies with the objective and the level of adaptation required and what is acceptable as residual, i.e. 'unmanaged' risk. Similarly, adaptation finance needs vary depending on the overall adaptation plans for the country, city, or region. It also depends on the assessment methods used. A 2023 study analysed country-level information submitted to the UNFCCC in National Adaptation Plans and Nationally Determined Contributions (85 countries). It estimated global adaptation needs of developing countries annual average to be US$387 billion, for the period up to 2030.

Both the cost estimates and needs estimates have high uncertainty. Adaptation costs are usually derived from economic modelling analysis (global or sectoral models). Adaptation needs are based on programme and project-level costing. These programmes depend on the high level adaptation instrument – such as a plan, policy or strategy. For many developing countries, the implementation of certain actions specified in the plans is conditional on receiving international support. in these countries, a majority (85%) of finance needs are expected to be met from international public climate finance, i.e. funding from developed to developing countries. There is less data available for adaptation costs and adaptation finance needs in high income countries. Data show that per capita needs tend to increase with income level, but these countries can also afford to invest more domestically.

Challenges and debates

Efficiency and equity

No consensus exists on who should bear the burden of adaptation and mitigation costs. Several different arguments have been made over how to spread the costs and benefits of taxes or systems based on emissions trading.

One approach considers the problem from the perspective of who benefits most from the public good. This approach is sensitive to the fact that different preferences exist between different income classes. The public good is viewed in a similar way as a private good, where those who use the public good must pay for it. Some people will benefit more from the public good than others, thus creating inequalities in the absence of benefit taxes. A difficulty with public goods is determining who exactly benefits from the public good, although some estimates of the distribution of the costs and benefits of global warming have been made – see above. Additionally, this approach does not provide guidance as to how the surplus of benefits from climate policy should be shared.

A second approach has been suggested based on economics and the social welfare function. To calculate the social welfare function requires an aggregation of the impacts of climate change policies and climate change itself across all affected individuals. This calculation involves a number of complexities and controversial equity issues. For example, the monetization of certain impacts on human health. There is also controversy over the issue of benefits affecting one individual offsetting negative impacts on another. These issues to do with equity and aggregation cannot be fully resolved by economics.

On a utilitarian basis, which has traditionally been used in welfare economics, an argument can be made for richer countries taking on most of the burdens of mitigation. However, another result is possible with a different modeling of impacts. If an approach is taken where the interests of poorer people have lower weighting, the result is that there is a much weaker argument in favour of mitigation action in rich countries. Valuing climate change impacts in poorer countries less than domestic climate change impacts (both in terms of policy and the impacts of climate change) would be consistent with observed spending in rich countries on foreign aid

A third approach looks at the problem from the perspective of who has contributed most to the problem. Because the industrialized countries have contributed more than two-thirds of the stock of human-induced GHGs in the atmosphere, this approach suggests that they should bear the largest share of the costs. This stock of emissions has been described as an "environmental debt". In terms of efficiency, this view is not supported. This is because efficiency requires incentives to be forward-looking, and not retrospective. The question of historical responsibility is a matter of ethics. It has been suggested that developed countries could address the issue by making side-payments to developing countries.

A 2019 modelling study found climate change had contributed towards global economic inequality. Wealthy countries in colder regions had either felt little overall economic impact from climate change, or possibly benefited, whereas poor hotter countries very likely grew less than if global warming had not occurred. Part of this observation stems from the fact that greenhouse gas emissions come mainly from high-income countries, while low-income countries are affected by it negatively. So, high-income countries are producing significant amounts of emissions, but the impacts are unequally threatening low-income countries, who do not have access to the resources to recover from such impacts. This further deepens the inequalities within the poor and the rich, hindering sustainability efforts. Impacts of climate change could even push millions of people into poverty.

Insurance and markets

See also: Climate change and insurance in the United States

Traditional insurance works by transferring risk to those better able or more willing to bear risk, and also by the pooling of risk. Since the risks of climate change are, to some extent, correlated, this reduces the effectiveness of pooling. However, there is reason to believe that different regions will be affected differently by climate change. This suggests that pooling might be effective. Since developing countries appear to be potentially most at risk from the effects of climate change, developed countries could provide insurance against these risks.

Disease, rising seas, reduced crop yields, and other harms driven by climate change will likely have a major deleterious impact on the economy by 2050 unless the world sharply reduces greenhouse gas emissions in the near term, according to a number of studies, including a study by the Carbon Disclosure Project and a study by insurance giant Swiss Re. The Swiss Re assessment found that annual output by the world economy will be reduced by $23 trillion annually, unless greenhouse gas emissions are adequately mitigated. As a consequence, according to the Swiss Re study, climate change will impact how the insurance industry prices a variety of risks.

Effects of economic growth and degrowth scenarios on emissions

The emissions of the richest 1% of the global population account for more than twice the combined share of the poorest 50%. Compliance with the 1.5 °C goal of the Paris Agreement would require the richest 1% to reduce their current emissions by at least a factor of 30, while per-person emissions of the poorest 50% could increase by a factor of about three.

Though total CO₂ emissions (size of pie charts) differ substantially among high-emitting regions, the pattern of higher income classes emitting more than lower income classes is consistent across regions. The world's top 1% of emitters emit over 1000 times more than the bottom 1%.

See also: Degrowth, Eco-economic decoupling, and Ecological economics

Economic growth is one of the causes of increasing greenhouse gas emissions. As the economy expands, demand for energy and energy-intensive goods increases, pushing up CO₂ emissions. On the other hand, economic growth may drive technological change and increase energy efficiency. Economic growth may be associated with specialization in certain economic sectors. If specialization is in energy-intensive sectors, then there will be a strong link between economic growth and emissions growth. If specialization is in less energy-intensive sectors, e.g. the services sector, then there might be a weak link between economic growth and emissions growth. In general, there is some degree of flexibility between economic growth and emissions growth.

Some studies found that degrowth scenarios, where economic output either declines or declines in terms of contemporary economic metrics such as current GDP, have been neglected in considerations of 1.5 °C scenarios reported by the Intergovernmental Panel on Climate Change (IPCC). They find that some degrowth scenarios "minimize many key risks for feasibility and sustainability compared to technology-driven pathways" with a core problem of such being feasibility in the context of contemporary decision-making of politics and globalized rebound- and relocation-effects. This is supported by other studies which state that absolute decoupling is highly unlikely to be achieved fast enough to prevent global warming over 1.5 °C or 2 °C, even under optimistic policy conditions.

See also

• Carbon price
• Climate finance
• Ecological economics
• Energy transition
• Environmental economics
• Social cost of carbon

References

Sources

• IPCC AR5 WG2 A (2014), Field, C.B.; et al. (eds.), Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II (WG2) to the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC), Cambridge University Press, archived from the original on 16 April 2014{{citation}}: CS1 maint: numeric names: authors list (link). Archived
• IPCC (2018). Masson-Delmotte, V.; Zhai, P.; Pörtner, H. O.; Roberts, D.; et al. (eds.). Global Warming of 1.5 °C. An IPCC Special Report on the impacts of global warming of 1.5 °C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty (PDF). Intergovernmental Panel on Climate Change.
• IPCC TAR WG2 (2001), McCarthy, J. J.; Canziani, O. F.; Leary, N. A.; Dokken, D. J.; White, K. S. (eds.), Climate Change 2001: Impacts, Adaptation and Vulnerability, Contribution of Working Group II to the Third Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, ISBN 978-0-521-80768-5, retrieved 2 August 2019{{citation}}: CS1 maint: numeric names: authors list (link) (pb: 0-521-01500-6).
• IPCC AR4 WG2 (2007), Parry, M.L.; Canziani, O.F.; Palutikof, J.P.; van der Linden, P.J.; Hanson, C.E. (eds.), Climate Change 2007: Impacts, Adaptation and Vulnerability, Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, ISBN 978-0-521-88010-7, archived from the original on 10 November 2018, retrieved 22 January 2012{{citation}}: CS1 maint: numeric names: authors list (link) (pb: 978-0-521-70597-4).

• House of Lords (21 June 2005), The Economics of Climate Change, the Second Report of the 2005-2006 session (HL 12-I and HL 12-II), produced by the UK Parliament House of Lords (HOL) Economics Affairs Select Committee, London, UK: The Stationery Office. High-resolution PDF versions: HL 12-I (report), HL 12-II (evidence).
• IPCC SAR WG3 (1996), Bruce, J. P.; Lee, H.; Haites, E. F. (eds.), Climate Change 1995: Economic and Social Dimensions of Climate Change (PDF), Contribution of Working Group III to the Second Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge, UK: Cambridge University Press, ISBN 978-0-521-56051-1{{citation}}: CS1 maint: numeric names: authors list (link) (pb: 0-521-56854-4)

• IPCC TAR WG3 (2001), Metz, B.; Davidson, O.; Swart, R.; Pan, J. (eds.), Climate Change 2001: Mitigation (PDF), Contribution of Working Group III to the Third Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, ISBN 978-0-521-80769-2, retrieved 17 January 2022{{citation}}: CS1 maint: numeric names: authors list (link) (pb: 0-521-01502-2).

• IPCC AR4 WG3 (2007), Metz, B.; Davidson, O. R.; Bosch, P. R.; Dave, R.; Meyer, L. A. (eds.), Climate Change 2007: Mitigation of Climate Change (PDF), Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, ISBN 978-0-521-88011-4{{citation}}: CS1 maint: numeric names: authors list (link) (pb: 978-0-521-70598-1).

External links

• Centre for Climate Change Economics and Policy at University of Leeds and London School of Economics.
• "The economics of climate change". 2020 lecture by William Nordhaus, Sterling Professor of Economics at Yale University
• "From Climate Crisis to Real Prosperity". 2020 Reith lecture by Mark Carney, COP26 finance advisor