Climate Change Economics

The Paris Agreement (PA) asserts that emissions pathways of greenhouse gases (GHG) should be consistent with holding the increase in global temperature below 1.5${}^{\circ }$C or 2${}^{\circ }$C above pre-industrial levels. New Zealand (NZ) committed to reduce emissions to 30% below 2005 levels by 2030. The purpose of this paper is to assess the range of economic costs for NZ derived from the commitment under the PA, conditional to the mitigation potential of forestry carbon sequestration (FCS), pricing agricultural emissions, and linking the NZ emissions tradable scheme (NZ ETS) to the European Union ETS (EU ETS). We use a general equilibrium model and “soft-link” it with the global timber model. We found that NZ can meet the PA terms; however, important GDP decreases may arise due to limited domestic mitigation potential from the energy and transport sectors. FCS plays a significant role in mitigating the negative impacts, where the benefits of FCS outweigh those of pricing agricultural emission and linking the NZ ETS.

China has set a goal of reducing its CO₂ intensity of GDP by 60–65% from the 2005 level in 2030 as its nationally determined contribution (NDC) under the Paris Climate Change Agreement. While the government is considering series of market and nonmarket measures to achieve its target, this study assesses the economic consequences if the target were to meet through a market mechanism, carbon tax. We used a dynamic computable general equilibrium model of China for the analysis. The study shows that the level of carbon tax to achieve the NDC target would be different depending on its design features. An increasing carbon tax that starts at a small rate in 2015 and rises to a level to meet the NDC target in 2030 would cause smaller GDP loss than the carbon tax with a constant rate would do. The GDP loss due to the carbon tax would be smaller when the tax revenue is utilized to cut existing distortionary taxes than when it is transferred to households as a lump-sum rebate.

Climate change impacts are stochastic and highly uncertain and moreover heterogeneous across regions. That is, there is a potential to sharing this risk ex-ante across regions and hence to reduce the welfare-economic costs of these risks. We analyze how climate risks could be reduced via an insurance scheme at the global scale across regions and quantify the potential welfare gains. We introduce risk sharing of global climate risk represented by the equilibrium climate sensitivity, and introduce an asset-based insurance scheme to allow for risk sharing across regions. We estimate that such risk sharing scheme could lead to welfare gains reducing the global costs of climate change by up to 15%. Such a scheme implies transfers of about USD 200 billion per year and faces important implementation challenges. The results provide a motivation for the loss and damage mechanism related negotiations about sharing risks of climate change at the global level.

We show that a policy maker who ignores regional data and instead relies on aggregated integrated assessment models is likely underestimating the carbon price and thus the required climate policy. Based on a simple theoretical model, we give conditions under which the Aggregation Dilemma is expected to play a role in climate change cost-benefit analysis. We then study the importance of the Aggregation Dilemma with the integrated assessment model RICE [Nordhaus and Boyer, (2000) Warning the World: Economic Models of Global Warming. MA: MIT Press]. Aggregating all regions of the RICE-99 model into one region yields a 40% lower social cost of carbon than the RICE model itself predicts. Based on extrapolating the results, a country-level integrated assessment model would give a more than eight times higher social cost of carbon compared to a fully aggregated model. We suggest that these tentative results require researchers to rethink the aggregation level used in integrated assessment models and to develop models at much lower levels of aggregation than currently available.