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We limit our scope to the emerging literature on the immediate short run and the direct and indirect long-run economic impacts of natural disasters. We exclude two sizeable and important literatures. First, we do not discuss studies that use hedonic pricing methods to link housing prices to natural disaster risks and mitigating factors see Barbier [ ] and Gopalakrishnan, Landry, and Smith [ ] for overviews. The remainder of the article is organized as follows. In the next section we present the theory that has been used to predict how natural disasters affect the macroeconomy.

Then we review computational models that have been used to simulate and quantify the predicted impacts from natural disasters, including catastrophe, input—output, computable general equilibrium, and integrated assessment models. Next we assess the methodologies and key findings of the empirical literature, including factors that mitigate disaster impacts. We conclude by identifying lessons for policymaking and discuss an agenda for future research in this area.

There is little need to theorize about direct disaster impacts. Shaking, inundation, and high winds simply cause damage; we discuss the measurement and prediction of such direct impacts in the next section. In this section we focus on the theoretical models that have been used to explain the indirect economic impacts of natural disasters. Typically the disaster is conceptualized as the sudden loss of production factors such as labor and capital , to which the economic system adjusts, either returning to the predisaster equilibrium or shifting to a new one.

Most research on the indirect impacts of natural disasters builds on the predictions of input—output I-O and computable general equilibrium CGE models. Both build on a social accounting matrix that identifies all monetary flows between all sectors in an economy. I-O models assume a time-invariant, fixed-proportions production function for all economic sectors and predict how damages in one sector affect trade and related production output in all of the others.

In contrast, CGE models assume stable behavior, reflected in stable demand and supply functions, and predict how natural disaster impacts change the demand, supply, and prices in various markets in equilibrium. Given these drawbacks of I-O and CGE models, several authors have derived and tested more sophisticated hypotheses based on neoclassical growth theory, 5 which is also used in integrated assessment models IAMs of climate change and the economy. In its simplest form, this theory assumes an aggregate production function using capital and labor with constant returns to scale , a fixed savings and depreciation rate, and diminishing returns to capital.

Such models predict a gradual return to the predisaster steady state after any shock to the capital stock or labor supply. In these models, natural disasters can have a lasting economic impact only if they permanently shift the basic parameters that determine the steady state, especially savings see Berlemann, Steinhardt, and Tutt , depreciation, or productivity growth.

A key limitation of neoclassical growth models is that they assume, rather than explain, technical change; endogenous growth models seek to address this limitation. Vintage capital models are an early branch of endogenous growth models that assume capital always embodies the best available technology at the time the capital is constructed.

Investment drives technology in these models, which predict that any accelerated depreciation of capital due to a disaster shock will result in higher productivity growth because technology will be updated.

In contrast, in AK models where A represents productivity and K refers to the capital stock , output and output per worker are linked to the level of accumulated capital in use, implying that negative capital shocks have a lasting negative impact on output per worker. Finally, in models of learning, knowledge accumulates in people as they produce more, and the level of productivity is assumed to depend on variables like cumulative production or investment. In these models, the destruction of capital or labor may stimulate learning and productivity growth during reconstruction, but this productivity is not embodied in the new capital as is the case in vintage capital models.

These early branches of endogenous growth models already allow for some productivity change over time in response to natural disasters. Nevertheless, few natural disaster applications use the more recent endogenous growth models. It is important to note that all of the types of macroeconomic growth models we reviewed 9 can be criticized for ignoring geography e. Because it now builds so heavily on mainstream macroeconomic models, the emerging literature on the economics of natural disasters is also vulnerable to such criticism.

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Thus, as we will discuss later, one direction for future research should be to consider regional economic models, which explicitly take geography into account Capello In fact, regional models of growth and development can be used to connect macrolevel indirect impacts e. The low probability that a natural disaster will occur in a particular area means that there are likely to be few historical observations for estimating losses. Moreover, the impacts of disasters are not always recorded in detail when disasters do occur.

This is why computational models are used to simulate potential impacts from hypothetical but realistic or historical natural disasters. Direct impacts are estimated using so-called catastrophe models, which, for instance, offer detailed results on property losses. Direct disaster impacts can then be fed into macroeconomic models that simulate indirect economic effects. Although these models do not provide precise predictions of economic effects after a disaster, they offer insights into economic processes that cause indirect impacts, from which lessons can be drawn about key vulnerable sectors and mitigating factors.

This section reviews catastrophe and macroeconomic model approaches and their key results in more detail.

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Catastrophe models use geographic information systems GISs to estimate the potential losses from specific natural disasters by simulating hypothetical physical characteristics of natural hazards, such as flood events, at a particular location. For instance, flood hazard maps indicate characteristics such as potentially flooded areas, inundation depths, and flow velocity for a flood with a specific probability of occurrence. Catastrophe models typically estimate the damage from natural hazards with various intensities and probabilities, from which annual expected damage is derived.

The geographic scales range from local e. Risk estimates from catastrophe models are used for a variety of purposes, including guiding the pricing of extreme weather insurance and informing public sector risk management strategies. Rather than providing estimates of ex post compensation for disaster losses, these studies provide information on the economic desirability of investing in reducing natural disaster risk ex ante. In their reviews of benefit—cost analyses of reducing natural disaster risk, Shreve and Kelman and Mechler find that although benefit—cost ratios differ significantly across contexts and risk reduction measures, they are typically well above unity, which means the measures are economically desirable.

In fact, according to Mechler , on average, the benefits of disaster risk reduction outweigh costs by a factor of four. Given the limited number of observations of natural hazard characteristics and losses per location, it is difficult to assess the external validity of catastrophe models i. Partly due to increased computing capabilities and the availability of data with a high spatial resolution, catastrophe modeling approaches have become increasingly refined.

In particular, the empirical basis for assumptions about the presence of protection infrastructure and the vulnerability of properties i. Moreover, catastrophe models typically assume that vulnerability is constant over time and independent of the behavior of governments and property owners. In reality, however, vulnerability is a dynamic process. For example, improvements may be made to better protect properties against natural disaster damage in response to disaster events or changes in the intensity or frequency of natural hazards due to climate change. Public authorities and emergency services may similarly learn and adapt.

With this in mind, recent research efforts have sought to improve the modeling of vulnerability by using agent-based models that combine catastrophe models with the behavioral economic decision making of agents involved in disaster preparedness and response e. This allows researchers to estimate how vulnerability changes in response to changing risks, the occurrence of disasters, or policy.

Macroeconomic models are used to estimate indirect losses from natural disasters, and include I-O and CGE models. In this section we review estimates from these models as well as from IAMs, which estimate natural disaster losses under climate change scenarios and have been used for guiding climate policy. As noted earlier, I-O models, which are based on matrices that capture the trade flows of the production inputs and outputs of different sectors in an economy, examine how natural disasters affect these trade flows and the related short-run production outputs Okuyama and Santos Several of the studies we reviewed use the inoperability I-O model often called the IIM , which captures the inoperability of a sector that is directly impacted by a natural disaster.

This inoperability distorts inputs supplied to other sectors, which causes indirect output losses and production costs and thus limits the final consumptions of goods.

A Computable General Equilibrium Model Based Analysis for India

This means that I-O models capture economic interdependencies between sectors that are upstream and downstream of the supply chain of disrupted goods within a national or regional economy. This allows the researcher to examine how a loss in an area directly impacted by a disaster ripples through to other sectors and regions. The simplicity of I-O models allows for the inclusion of sectoral detail and a simple representation of local economic disaster effects. Moreover, the improved availability of data allows for a high spatial aggregation as well as an ability to downscale models to more detailed spatial scales.

However, standard I-O models do not capture certain economic mechanisms that may influence the final outcomes of disaster impacts, such as supply side shocks on sectors that have specific production constraints, price changes that influence the demand for final and intermediate goods, technology changes that affect intermediate input requirements, input and import substitution, and adaptive behavior and other forms of economic resilience e.

GHG Emissions and Economic Growth: A Computable General Equilibrium Model Based Analysis for India

This means that I-O models may be oversimplified. Several recent models, such as the adaptive regional input—output ARIO model, have sought to overcome these shortcomings of standard I-O models. Methodological innovations of the ARIO model include modeling price increases after a disaster which limits demand , imposing sector-specific supply constraints or the use of overcapacity, adjusting the shape and duration of recovery periods, or including specific resiliency measures.

Some of these studies find high indirect economic losses. For example, using the ARIO model, Hallegatte estimates that indirect losses account for 30 percent of the direct losses from Hurricane Katrina, that these losses increase nonlinearly with direct losses, and that they can even surpass them for extreme disasters. Another recent I-O model, the multiregional impact assessment MRIA model, shows that indirect losses depend on the geographic scale over which the impacts are estimated. For example, Koks and Thissen show that although an I-O model of an extreme flood event in Rotterdam harbor estimates high indirect losses which exceed the direct losses , an MRIA model of the same event finds substantially smaller indirect losses because of substitution effects that increase output in regions that are not directly impacted.

Moreover, the way in which resilience measures are modeled substantially influences I-O outcomes. This is illustrated by Rose and Wei , who use a demand and supply driven I-O model to estimate the losses from a disaster that causes disruptions to sea ports in Texas. They find that indirect losses depend significantly on the modeling of resilience measures, which mitigate the impacts of a port disruption at the impacted site or along the supply chain.

More specifically, allowing for resilience measures e. Overall, I-O studies show that although local economic losses from natural disasters can be important for certain sectors, the broader macroeconomic system has an inherent flexibility that moderates the aggregate impacts. In particular, negative impacts are at least partly offset by substitution, which results in increased production by companies that are not directly impacted and increased production for reconstruction.

A consistent picture that emerges from sensitivity analyses that were conducted for the models we reviewed is that uncertainties are high and results largely depend on assumptions about resilience measures and recovery paths. CGE models provide a more flexible model framework than I-O models because they include demand and supply in various markets in equilibrium 14 and they are nonlinear e.

CGE models usually simulate the impacts of natural disasters on economic activity by estimating how disruptions to the supply of goods and services affect GDP through relative price and quantity changes and considering input and import substitution possibilities for the demand of intermediate and final consumption goods.

Because of this price flexibility, which typically represents long-run processes, it has been argued that CGE models are better able to represent the long-run economic consequences of natural disasters than I-O models Rose and Liao CGE models have been applied to a variety of natural disasters at the global, national, and local levels.

Several CGE models of natural disasters have a more detailed spatial dimension i. More specifically, several studies have used a catastrophe model to estimate direct disaster impacts, which are then integrated into a regional CGE framework. They find that the direct flood impacts occur in northern Italy, where there are also large indirect losses. These indirect losses are partly offset by small economic gains in areas not directly affected by the flood, which take over some of the disrupted production. They find that the agricultural impacts of droughts and floods cause national economic losses that range from 1.

These impacts exacerbate income inequality and poverty at the household level. CGE models have also been used to examine various resilience strategies that could significantly reduce losses from disaster events. For example, Rose and Liao show that the economic costs from the disruption of water supply during the Northridge earthquake could have been greatly reduced through water conservation and substitution, and that a mitigation strategy that replaces vulnerable pipes reduces total losses by almost half.

Overall, the flexibility of CGE models in terms of substitution possibilities and price changes that balance demand and supply makes them more suitable for studying the long-run economic consequences of disasters. Due to these characteristics, the sometimes high ratios of indirect to direct disaster losses in I-O models are not observed in these CGE applications, which highlights the important role of economic adjustment processes in limiting indirect disaster impacts.

Several global but often regionally differentiated IAMs of climate change and the economy have been developed that estimate the impacts of climate change in GDP terms, estimate the social cost of carbon, and derive economically optimal pathways for reducing greenhouse gas emissions. These models are based on a simplified version of neoclassical economic growth theory, because, with the exception of Dietz and Stern , they assume exogenous economic growth in relation to climate change. Although most IAMs estimate the aggregate economic impacts of climate change, some applications have focused on natural disasters.

Diaz and Keller adapt the DICE model to estimate the economic impacts of a disintegration of the West Antarctic Ice Sheet due to climate change which causes an additional sea level rise of 3. They find that the current optimal climate policy is largely insensitive to this disintegration because, due to discounting, the costs in the far future have almost no influence on present value costs. Others have argued that for intergenerational equity, lower discount rates should be assumed, which implies greater weights on future climate change impacts Stern Using the PAGE model, Dietz shows that the use of a lower discount rate is an important assumption when including more extreme climate change risks, because a low discount rate substantially increases the present value of the economic costs of these risks.

Developing a CGE model of the Indian economy

A number of studies have reviewed the use of IAMs as tools for providing guidance about climate policy, including Stern , van den Bergh and Botzen , and Tol These reviews highlight the great uncertainty of economic impacts, which is due partly to the incomplete or ad hoc inclusion of specific climate change risks in simplified damage functions.

Moreover, it has been argued that the treatment of natural disasters in IAMs is incomplete and that current impact functions insufficiently capture the economic costs of sea level rise and extreme weather Ackerman and Munitz and hence need to be updated. With this background on theoretical and computational models, we next turn to a review of empirical studies of the economic impacts of natural disasters.

First we discuss data sources and econometric methods. This is followed by a discussion of the main results concerning disaster impacts and mitigating factors. Limitations of EM-DAT include that it has variable thresholds for inclusion of events in the database and that damages are recorded as uncorrected monetary estimates from local authorities, which may be inflated shortly after a disaster. Disaster intensity measures from EM-DAT are likely to be correlated with GDP per capita, which is the main dependent variable in the literature, because losses are generally higher and better recorded in developed countries.

Given these data issues, many recent studies use definitions of natural disasters based on geophysical or meteorological variables, such as hurricanes e. Such physical indicators of natural disasters are not subject to the endogeneity bias of the EM-DAT data and thus should be the data used in future research. The wide range of possible direct and indirect impacts of natural disasters is reflected in the wide range of economic outcome data used in the literature. These outcome data include GDP, GDP growth rate, trade flows, death counts, employment, per capita income, expenditures, migration, housing and other asset values, and government transfers.

Most estimates of the economic impacts of natural disasters are based on regressions of aggregate variables measured at the country level on some measure of disasters, such as the number of disasters, the monetary damages, the number of fatalities, or hurricane intensity. Published in: Journal of Environmental Studies , Vol.

Published in: Journal of Trade Studies , Vol. Mohamed, Issam A. Nakada, Yoshiaki : Factor intensities and factor substitution in general equilibrium: A Comment. Necibi, Thameur : The oil position in the Tunisian economy: Adaptation of computable general equilibrium model. Ngouhouo, Ibrahim and Tchoffo, Rodrigue : Real level of public investment: how to manage the inflation? Opatija - Croatia: University of Rijeka. April CD-Book: Session 6 15 pp. Published in: 18 July Radkov, Petar : An interest rate model with Markov chain volatility level.

Forthcoming in:. Raihan, Selim : Implications of the global economic crisis for the Bangladesh economy. Raihan, Selim and Razzaque, Mohammad A : WTO and regional trade negotiation outcomes: quantitative assessments of potential implications on Bangladesh. Published in: June Book Chapter. Published by Cameron May Ltd, London Ramsey, David M.

Published in: Banach Center Publications , Vol. Published in: 21 October Romero, Carlos A. Rutten, Martine M. Schenker, Oliver : How uncertainty reduces greenhouse gas emissions. Schenker, Oliver : Transporting goods and damages. The role of trade on the distribution of climate change costs. Serbanoiu, Georgian Valentin : Transmission of fiscal policy shocks into Romania's economy. Chapter 3 : pp. Published in: , Vol. Workin, : pp. Siddiqui, Rizwana and Kemal, A.

Skribans, Valerijs : Development of the Latvian energy sector system dynamic model.

The result shows that the GDP would attain a slightly higher growth rate of 0. GDP reductions ranging from 2. The GDP loss could go as high as As the GHG emission reduction targets encourage the use of more efficient and low-carbon technologies requiring higher investments, they lead to the distortions in the future GDP. The results of the sensitivity analysis show that the expansion of alternative energy industries and energy efficient technologies would help to change the input-output relations and counteract some of the GDP losses.

The sensitivity analysis shows that the GDP loss could be lowered up to 2. Government and household final consumption expenditure forms the major component in total GDP. Together, they constituted a share of about The GHG emission reduction targets cause a significant increment in the government consumption and a drastic decline in the household consumption, more specifically in the cases of ambitious reduction scenarios.

With the increasing emission reduction targets, the cumulative government consumption during to would increase by However, compared to BAU, it would cause a drastic decline in the cumulative household consumption by The increasing reductions in the household consumption with rising levels of GHG emission reduction relates to the increasing welfare loss when compared to the BAU scenario. As such, the welfare loss would tend to increase from This shows that the economy will face severe damage if the emission reduction targets specified by the NDC and beyond are imposed without considering the technological improvements.

The study thus found that increasing the deployment of energy-efficient technologies could help in improving the household consumption thereby lowering the welfare losses up to Government plays an important role when the economy is in recession and should increase spending in order to stimulate economic activities. The modeling result shows that the increasing emission reduction targets tend to increase government spending on welfare benefits, education, research and training, industries, petroleum, electricity sector, transport, infrastructure investments, etc.

The results show that the increase in the total governmental consumption would vary from The service sector that comprises banking and insurance, real estate, business, and public services including public administration, education, research, sanitary, hospitals, restaurants, and hotels, would have the largest share in the total government and household consumption both in the BAU and the alternative scenarios see Fig.

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The service sector would face an increasing share ranging from With the varying shares of The shares of the petroleum and electricity industries in the total consumption would increase from 1. The total output is estimated to reduce by 5. The study shows that the total sectoral output would increase by 3. The study showed that the GHG emission reduction targets tend to increase the production from agriculture and forestry, construction, and services sectors. The study found that the production from the agriculture and forestry sector i.

This shows that the increasing levels of GHG mitigation promotes higher production from the forestry sector, highlighting the need of increasing use of biomass-based technologies to meet the zero emissions target in The service and the construction sectors would experience an increase of about The increase in the GHG emission reduction targets tends to mostly impact the fossil fuel industries, thereby promoting higher use of renewable energies from to Basically, the coal and lignite mining industries would suffer a heavy output cut by The petroleum refineries and gas industries would have to reduce their productions by The simulation results identified that the electricity sector would have to reduce its output by Results suggest that in the absence of any other policy measures, the country would have to rely heavily on imported electricity to meet its demand in order to attain the desired level of emission reductions.

Being carbon-intensive sectors, the transport and industry sectors output would be affected significantly by the GHG mitigation targets. The GHG emission reduction target would result in decreasing output of the transport sector from The industrial output would be reduced from 8. The GHG emissions from the electricity sector would reduce by The GHG emissions from the industry sector will be reduced by The sensitivity analysis performed in this study suggests that such large GHG emission reductions would only be possible if efficient technologies and technologies based on renewable energies are deployed in all the economic sectors.

For example, the use of clean and energy-efficient technologies such as electric, biofuel, and hybrid vehicles in the transport sector and CCS technologies including bioenergy in the industry and electricity generation sectors would help to curb large amounts of GHG emissions in the country [ 9 , 10 , 30 ]. The electricity sector had the major share of With a share of The industry sector would attain a share of Imposition of GHG mitigation targets would reduce the shares in the total GHG emissions from the electricity and industry sectors to Conversely, the shares of the transport and service sectors would increase to The GHG emission intensity, measured in terms of emission per unit GDP, is a commonly used indicator to assess the linkage between the GHG emissions and economic growth across countries and across different scenarios within the same country.

The GHG emission intensity would vary from 0. It is noted that the GHG prices would decline after in all the constant emission reduction scenarios. A higher GHG price leads to a larger reduction in the household consumption of goods and services as well as demands for a switch to cleaner energy resources and technologies. The study found that in order to lower the GHG prices, the economy needs to increase the deployment of more energy-efficient technologies.

This shows the need of encouraging investments in the low-carbon technology options for lowering the GHG prices and limiting the macroeconomic loss. The increasing GHG emission reduction targets will not much alter the total amount of capital requirement during to ; however, such mitigation targets will change the sectoral composition of the total capital requirement. The industry sector had the highest share i. Except for the agricultural and forestry sector, the GHG emission reduction targets will cause a decrement in shares of most of the sectors in the total capital requirements in the ERT20 to ERT50—90 scenarios.

The industrial sector, whose share in the total capital requirement would decline from The agriculture and forestry sector will have the highest share of This shows that to achieve zero emissions by requires heavy investments in the forestry sector which acts as a potential carbon sink to absorb GHG emissions. This emphasizes the need of investment required for the deployment of biomass-based CCS technologies to curve out the GHG emissions towards meeting the net zero goal in The substantial production growth in the construction, coal and lignite, electricity, industries, trade and service sectors are the major cause behind the rising exports and imports in the BAU.

The ambitious reduction targets of ERT20—90 to ERT50—90 would result in a decreasing demand of both the exports and imports in However, the limitation of technological progress would cause a substantial increase in both the exports and imports in under the ERT20—90 and ERT25—90 scenarios. However, the increase in the demand of both the exports and imports can be lowered up to 5. This paper was drawn with the aim to analyze the macroeconomic impacts of various low to high GHG emission reduction targets.

Studies suggest that the soonest possible attainment of the global peak in GHG emissions is necessary to achieve the long-term temperature goal in order to reduce the intensity of mitigation efforts that would be required due to a delayed peak [ 35 ]. However, Thailand should put more effort in mitigation actions to achieve peak emissions by Lowering the activity level of the energy-intensive industries, improving end-use energy efficiency, switching fuel, deploying CCS technologies in the power and industrial sectors, and expanding renewable energy-based technologies are identified to be important mitigation measures for Thailand in attaining such an emissions peak.

The sensitivity analysis undertaken as a part of this study shows that the expanding alternative energy technologies and shifting towards more energy-efficient technologies would bring about positive impacts in terms of lowering government consumption, welfare losses, sectoral output, and sectoral GHG emissions. The study showed that such technological changes not only accelerates abatement but would also help to counteract the GDP losses and even lower the price of GHG emissions. This study however has several limitations. The simulation results are based on the input-output data of with underlying assumptions of fixed technological coefficients, constant return of scale, no constraints on resources, and efficient employment of all local resources.

Nuclear-based power generation, which may be a potential option to abate GHG emissions, is not considered in this analysis. The simulation results are based on the fact of attaining the imposed level of GHG emission reduction targets in the presence of limited technological progress. In the absence of any other policy strategy besides providing a maximum GHG emission constraint, the model shows that the output reduction is the only option to reduce emissions, thereby increasing the economic costs of reaching more ambitious emission reduction targets.

Such phenomenon of GHG price reductions have even been explained by [ 11 ]. Due to the assumptions of constant input-output coefficients, the model has limitations for shifting towards less emission intensive inputs with GHG reduction targets. As such, the ambitious GHG mitigation targets call for a substantially high level of productions from the agriculture and forestry sectors to meet the demand.

However, expansion of productions from the agriculture and forestry sectors not only demands a larger area of landmass but also requires technological development to efficiently use bioenergy resources. The results also highlight that the implementation of large-scale afforestation would be necessary for carbon sequestration. However, this study does not provide limits on the availability of land, energy, and water resources. And incorporation of such limitations would definitely change the magnitude of production from the agriculture and forestry sectors to yield more realistic results.

But still, this provides an insight that the agriculture and forestry sector could play a significant role in fostering GHG mitigation opportunities for Thailand. The NDCs of Thailand do not comprehensively include agriculture and forestry in their mitigation targets. As such, government should revise and formulate ambitious renewable energy goals by incorporating agriculture and forestry mitigation targets and measures in the NDC to meet higher emission reductions goal [ 57 ]. Results show the requirement of heavy output reductions from the electricity generation sector with the imposition of GHG reduction targets under the technology-constrained scenarios.

The study also showed that the output reductions could be lowered by considering technological advancements. The electricity generation sector in Thailand is dominated by natural gas with a share of In order to reduce dependence on a single country Myanmar for its natural gas imports, the country has set ambitious targets to diversify its electricity generation mainly based on coal and renewables [ 50 ]. As such, adoption of CCS technologies in both the fossil fuel-fired and biomass-based power plants, along with the increment in other forms of renewable energy-based power generations would provide significant potential to lower GHG emissions from the electricity industry.

However, uncertainties and challenges still remain in the wide adoption of CCS technology in both the electricity and manufacturing industries, and in power quality instability of using significant amount of renewable energy-based electricity generation [ 30 ]. The cost of mitigation and macroeconomic loss could be lowered by maintaining a clear communication between the government and the private sector which would help in rapid penetration of renewable energy and energy-efficient technologies, thereby stimulating private sector investments [ 33 ].

The abilities to implement GHG mitigation measures could be enhanced through international support in the form of finance, technology transfer, capacity building, and raising awareness and adaptation related to climate change. This study found that in the absence of transformative structural and technological changes, more stringent GHG emission reduction targets would impose more challenges to the energy and economic systems of the country and would lead to greater GDP and welfare losses compared to the BAU scenario.

In particular, such targets would cause a decline in the final consumption of households causing subsequent effects in the economic development of the country. The absence of any policy strategy besides imposing a maximum GHG emissions goal would cause a decline in the production output, especially from the carbon intensive industries such as coal and lignite mining, petroleum refineries, gas industries, electricity, transport, and industry sectors. Being carbon intensive, the GHG emission reduction targets would cause a deep decline in the emission shares of the electricity and industry sectors.

However, an appropriate energy policy plan with the effective deployment of renewable energy such as biofuels, biomass, solar, wind , CCS-based technologies, and energy-efficient options could lessen the challenges of macroeconomic loss and even help to overcome the GHG price distortions.