This paper explores global perceptions and understanding of climate change and policies, examining factors that influence support for climate action and the impact of different types of information. We conduct large-scale surveys with 40,000 respondents from 20 countries, providing new international data on attitudes toward climate change and respondents’ socioeconomic backgrounds and lifestyles. We identify three key perceptions affecting policy support: perceived effectiveness of policies in reducing emissions, their impact on low-income households, and their effect on respondents’ households (self-interest). Educational videos clarifying policy mechanisms increase support for climate policies; those merely highlighting climate change’s impacts do not.

Notwithstanding great progress in scientific and economic understanding of climate change, it has proven difficult to forge international agreements because of free-riding, as seen in the defunct Kyoto Protocol. This study examines the club as a model for international climate policy. Based on economic theory and empirical modeling, it finds that without sanctions against non-participants there are no stable coalitions other than those with minimal abatement. By contrast, a regime with small trade penalties on non-participants, a Climate Club, can induce a large stable coalition with high levels of abatement.

This paper uses international trade data to examine the effects of climate shocks on economic activity. At the aggregate level, Melissa Dell, Benjamin F. Jones, and Benjamin A. Olken (2008) (hereafter, DJO) have demonstrated that higher temperatures in a given year reduce the growth rate of GDP per capita, but only in poor countries. The analysis of trade data in this paper builds on that finding, with three principal motivations. First, international trade links the fortunes of countries, providing potentially important conduits for geographically limited climatic impacts to have global economic effects. Second, international trade data is the best available source for identifying economic activity worldwide separately by narrowly defined sectors. Examining international trade data, one can thus say more precisely what sectors are affected by climatic changes. Finally, the trade data, collected by the importing country, provides a check on the potentially low-quality national accounts data provided by the home country.

There is a growing consensus that emissions of greenhouse gases due to human activity will alter the earth’s climate, most notably by causing temperatures, precipitation levels, and weather variability to increase. The design of optimal climate change mitigation policies requires estimates of the health and other benefits of reductions in greenhouse gases; current evidence on the magnitude of the direct and indirect impacts, however, is considered insufficient for reliable conclusions (A. J. McMichael et al. 2003).

Manmade climate change (CC) has catastrophic consequences. The United States has already experienced wholesale population realignment due to climate as households have relocated to the Sunbelt and West. The irony is that people are moving toward the heat and major storms associated with CC. As CC intensifies, with high rates of internal US factor mobility, firms and households will likely again relocate to areas with higher utility and profits, reducing CC costs. Yet current research typically focuses on CC costs in a given location without considering this realignment. We propose several avenues to overcome such shortcomings in US CC modeling.

We study the welfare impacts of 96 climate-related tax and spending policies. We extend and apply the marginal value of public funds (MVPF) framework, most notably providing a new method for incorporating learning-by-doing spillovers. We find subsidies for the production of clean energy (such as wind production tax credits) have higher MVPFs than all other subsidies in our sample, including EV subsidies. Conservation nudges have large MVPFs when targeting regions with dirty grids. Fuel taxes and cap-and-trade policies are highly efficient means of raising revenue. We also construct traditional cost-per-ton estimates and compare and contrast the lessons they provide.

Our goal is to evaluate variations in rice yields for likely climate changes for Southeast Asia. To do so we integrate economic modeling with soil science crop growth simulation model, weather simulation model and global climate change models. We estimate impacts of climate change under two different climate scenarios, one assuming high future global anthropogenic pollution emissions,

Lemoine and Rudik (2017) argues that it is efficient to delay reducing carbon emissions, due to supposed inertia in the climate system’s response to emissions. This conclusion rests upon misunderstanding the relevant earth system modeling: there is no substantial lag between CO 2 emissions and warming. Applying a representation of the earth system that captures the range of responses seen in complex earth system models invalidates the original article’s implications for climate policy. The least-cost policy path that limits warming to 2°C implies that the carbon price starts high and increases at the interest rate. It cannot rely on climate inertia to delay reducing and allow greater cumulative emissions.

This paper measures the economic impact of climate change on US agricultural land by estimating the effect of random year-to-year variation in temperature and precipitation on agricultural profits. The preferred estimates indicate that climate change will increase annual profits by $1.3 billion in 2002 dollars (2002$) or 4 percent. This estimate is robust to numerous specification checks and relatively precise, so large negative or positive effects are unlikely. We also find the hedonic approach—which is the standard in the previous literature—to be unreliable because it produces estimates that are extremely sensitive to seemingly minor choices about control variables, sample, and weighting.

In a series of studies employing a variety of approaches, we have found that the potential impact of climate change on US agriculture is likely negative. Deschênes and Greenstone (2007) report dramatically different results based on regressions of agricultural profits and yields on weather variables. The divergence is explained by (1) missing and incorrect weather and climate data in their study; (2) their use of older climate change projections rather than the more recent and less optimistic projections from the Fourth Assessment Report; and (3) difficulties in their profit measure due to the confounding effects of storage.