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In the recent United Nations Framework Convention on Climate Change (UNFCCC) negotiations, sectoral trading was proposed to encourage early action and spur investment in low carbon technologies in developing countries. This mechanism involves including a sector from one or more nations in an international cap-and-trade system. We analyze trade in carbon permits between the Chinese electricity sector and a US economy-wide cap-and-trade program using the MIT Emissions Prediction and Policy Analysis (EPPA) model. In 2030, the US purchases permits valued at $42 billion from China, which represents 46% of its capped emissions. In China, sectoral trading increases the price of electricity and reduces aggregate electricity generation, especially from coal. However, sectoral trading induces only moderate increases in generation from nuclear and renewables. We also observe increases in emission from other sectors. In the US, the availability of cheap emissions permits reduces the cost of climate policy and increases electricity generation.

Accounting for socioeconomic and demographic variables, as well as country-specific effects, households' marginal willingness to pay for climate is revealed using European data on life-satisfaction. Individuals located in areas with lower average levels of sunshine and higher average levels of relative humidity are less satisfied as are individuals in locations subject to significant seasonal variation in monthly mean temperatures and rain days. Ranking regions by climate households appear strongly to favor the Mediterranean climate over the climate of Northern Europe.

The climate sensitivity of electricity demand in India is likely to be highly sensitive to growth in income. Thus, both intensive and extensive adjustments in cooling and heating will play an important role in determining future climate change impacts on electricity demand. This paper utilizes a national level panel dataset of 28 Indian states for the period 2005–2009. The preferred estimates indicate that climate change will increase electricity demand by 6.7% with 4% p.a. GDP growth and 8.5% with 6% p.a. GDP growth in 2030 over the reference scenario of no climate change. This reflects the fact that the estimated marginal effect of a hotter climate is greater when income is higher. Over 50% of the climate change impacts will be due to extensive adjustments as the current penetration of space conditioning equipments such as air conditioners is very low.

Climate change will push the weather experienced by people affected outside the bounds of historic norms, resulting in unprecedented weather events. But people and firms should be able to learn from their experience of unusual weather and adjust their expectations about the climate distribution accordingly. The efficiency of this learning process gives an upper bound on the rate at which adaptation can occur and is therefore important in determining the adjustment costs associated with climate change. Learning about climate change requires people to infer the state of a changing probability distribution (climate) given annual draws from that distribution (weather). If the climate is stationary, it can be inferred from the distribution of historic weather observations, but if it is changing, the inference problem is more challenging. This paper first develops different learning models, including an efficient hierarchical Bayesian model in which the observer learns whether the climate is changing and, if it is, the functional form that describes that change. I contrast this with a less efficient but simpler learning model in which observers react to past changes but are unable to anticipate future changes. I propose a general metric of learning costs based on the average, discounted squared difference between beliefs and the true climate state and use climate model output to calculate this metric for two emissions scenarios, finding substantial relative differences between learning models and scenarios but small absolute values. Geographic differences arise from spatial patterns of warming rates and natural weather variability (noise). Finally, I present results from an experimental game simulating the adaptation decision, which suggests that people are able to learn about a trending climate and respond proactively.

Future carbon dioxide (CO₂) emissions under a carbon tax depend on the time-path of the economy under baseline (business-as-usual) conditions as well as the extent to which the policy reduces emissions relative to the baseline. Considerable uncertainties surround the baseline forecasts for fuel prices, energy efficiency (energy-GDP ratios), and GDP, as evidenced by the significant ranges in the forecasts by government agencies and research institutions in the U.S. This paper assesses the significance of these uncertainties to the path of CO₂ emissions under a carbon tax. We do this by examining the emissions levels and quantities of abatement that result from the E3 general equilibrium model under a range of alternative baseline forecasts for fuel prices, energy efficiency, and GDP, where the different baselines are produced through suitable changes to key model parameters. In addition, we consider how the time-profile of the carbon tax needed to achieve specified CO₂ abatement targets is affected by such forecast-linked changes in parameters.

We find that the sensitivity of baseline emissions to alternative forecasts depends on the particular forecasted variable under consideration. Baseline CO₂ emissions are highly sensitive to alternative scenarios related to the rate of energy efficiency improvements in the nonenergy sector and the rate of general economic growth. In contrast, such emissions are much less sensitive to alternative scenarios related to the productivity of fossil fuel production. The extent of abatement from the baseline is generally fairly insensitive to changes in the scenarios for time-paths of fuel prices, energy-efficiency and GDP. We also find that short-term emissions targets can be achieved with relatively moderate carbon taxes under all of the baseline scenarios considered.

This paper conceptualizes the soil conservation decisions of farmers when confronted with climate uncertainty. Using a dynamic stochastic optimization model with uncertainty captured by climate variability, soil conservation investment is assessed within the framework of an investment adjustment cost model. The theoretical results reveal that the effects of an uncertain future climate on the optimal path of soil conservation investment depend on how sensitive production is to climate and that input and output prices have level effects on the optimal path of investment. An empirical application to data from Texas shows that depending on the sensitivity of production to climate and the severity of the risk; climate induced uncertainty may have a negative effect, a threshold (U-shaped) effect, or no effect at all on soil conservation investment.

The United States is currently on pace to fall well short of its promises to reduce greenhouse gas emissions by 26–28%, relative to 2005, by 2025, under the UN Framework and Convention on Climate Change (UNFCCC) Paris Agreement, even if President Trump did not eliminate most Obama-era climate regulations. However, there still exists interest in reducing emissions, especially from some members of Congress, and there are a number of federal policy options to reduce greenhouse gas emissions if Congress (or a new administration in 2021) so chooses. In this paper, we show that a federal economy-wide carbon tax on US carbon dioxide emissions could significantly contribute to the reductions necessary to fulfill the US international climate commitments. Using a detailed multi-sector computable general equilibrium (CGE) model, we predict the carbon price paths that would be necessary to meet the 28% emissions target and show the economic costs of such carbon-pricing policies. We then demonstrate how both the price paths and associated costs change if action is delayed.

About 140 countries have announced or are considering net zero targets. To explore the implications of such targets, we apply an integrated earth system–economic model to investigate illustrative net zero emissions scenarios. Given the technologies as characterized in our modeling framework, we find that with net zero targets afforestation in earlier years and biomass energy with carbon capture and storage (BECCS) technology in later years are important negative emissions technologies, allowing continued emissions from hard-to-reduce sectors and sources. With the entire world achieving net zero by 2050 a very rapid scale-up of BECCS is required, increasing mitigation costs through mid-century substantially, compared with a scenario where some countries achieve net zero by 2050 while others continue some emissions in the latter half of the century. The scenarios slightly overshoot 1.5^∘C at mid-century but are at or below 1.5^∘C by 2100 with median climate response. Accounting for climate uncertainty, global achievement of net zero by 2050 essentially guarantees that the 1.5^∘C target will be achieved, compared to having a 50–50 chance in the scenario without net zero. This indicates a tradeoff between policy costs and likelihood of achieving 1.5^∘C.

Following the UK’s hosting of the United Nations Convention of the Parties Climate Summit in 2021, political targets for reducing greenhouse gas emissions — “Net-Zero” — have gained momentum. We address the gap in how public preferences are accounted for in climate decision-making by applying Contingent-Valuation techniques which ask people to state their Willingness-to-Pay (WTP) for the UK’s 2050 Net-Zero target. Mean WTP is £37.57/household to support Net-Zero (median £11.25), with a present-value of £2.3 billion across UK households. While younger people are more likely to experience the long-term impacts of climate change, older generations are willing to pay more to support it, suggesting that public support for Net-Zero is largely based on “nonuse” benefits, rather than direct “use” benefits to oneself. The COVID-19 epidemic affected WTP bids in a quarter of respondents. Finally, we explore how choice of positive or normative discount rate affects policy conclusions when monetizing consumer preferences.