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Article

Jennifer L. Castle and David F. Hendry

Shared features of economic and climate time series imply that tools for empirically modeling nonstationary economic outcomes are also appropriate for studying many aspects of observational climate-change data. Greenhouse gas emissions, such as carbon dioxide, nitrous oxide, and methane, are a major cause of climate change as they cumulate in the atmosphere and reradiate the sun’s energy. As these emissions are currently mainly due to economic activity, economic and climate time series have commonalities, including considerable inertia, stochastic trends, and distributional shifts, and hence the same econometric modeling approaches can be applied to analyze both phenomena. Moreover, both disciplines lack complete knowledge of their respective data-generating processes (DGPs), so model search retaining viable theory but allowing for shifting distributions is important. Reliable modeling of both climate and economic-related time series requires finding an unknown DGP (or close approximation thereto) to represent multivariate evolving processes subject to abrupt shifts. Consequently, to ensure that DGP is nested within a much larger set of candidate determinants, model formulations to search over should comprise all potentially relevant variables, their dynamics, indicators for perturbing outliers, shifts, trend breaks, and nonlinear functions, while retaining well-established theoretical insights. Econometric modeling of climate-change data requires a sufficiently general model selection approach to handle all these aspects. Machine learning with multipath block searches commencing from very general specifications, usually with more candidate explanatory variables than observations, to discover well-specified and undominated models of the nonstationary processes under analysis, offers a rigorous route to analyzing such complex data. To do so requires applying appropriate indicator saturation estimators (ISEs), a class that includes impulse indicators for outliers, step indicators for location shifts, multiplicative indicators for parameter changes, and trend indicators for trend breaks. All ISEs entail more candidate variables than observations, often by a large margin when implementing combinations, yet can detect the impacts of shifts and policy interventions to avoid nonconstant parameters in models, as well as improve forecasts. To characterize nonstationary observational data, one must handle all substantively relevant features jointly: A failure to do so leads to nonconstant and mis-specified models and hence incorrect theory evaluation and policy analyses.

Article

The analysis of convergence behavior with respect to emissions and measures of environmental quality can be categorized into four types of tests: absolute and conditional β-convergence, σ-convergence, club convergence, and stochastic convergence. In the context of emissions, absolute β-convergence occurs when countries with high initial levels of emissions have a lower emission growth rate than countries with low initial levels of emissions. Conditional β-convergence allows for possible differences among countries through the inclusion of exogenous variables to capture country-specific effects. Given that absolute and conditional β-convergence do not account for the dynamics of the growth process, which can potentially lead to dynamic panel data bias, σ-convergence evaluates the dynamics and intradistributional aspects of emissions to determine whether the cross-section variance of emissions decreases over time. The more recent club convergence approach tests the decline in the cross-sectional variation in emissions among countries over time and whether heterogeneous time-varying idiosyncratic components converge over time after controlling for a common growth component in emissions among countries. In essence, the club convergence approach evaluates both conditional σ- and β-convergence within a panel framework. Finally, stochastic convergence examines the time series behavior of a country’s emissions relative to another country or group of countries. Using univariate or panel unit root/stationarity tests, stochastic convergence is present if relative emissions, defined as the log of emissions for a particular country relative to another country or group of countries, is trend-stationary. The majority of the empirical literature analyzes carbon dioxide emissions and varies in terms of both the convergence tests deployed and the results. While the results supportive of emissions convergence for large global country coverage are limited, empirical studies that focus on country groupings defined by income classification, geographic region, or institutional structure (i.e., EU, OECD, etc.) are more likely to provide support for emissions convergence. The vast majority of studies have relied on tests of stochastic convergence with tests of σ-convergence and the distributional dynamics of emissions less so. With respect to tests of stochastic convergence, an alternative testing procedure accounts for structural breaks and cross-correlations simultaneously is presented. Using data for OECD countries, the results based on the inclusion of both structural breaks and cross-correlations through a factor structure provides less support for stochastic convergence when compared to unit root tests with the inclusion of just structural breaks. Future studies should increase focus on other air pollutants to include greenhouse gas emissions and their components, not to mention expanding the range of geographical regions analyzed and more robust analysis of the various types of convergence tests to render a more comprehensive view of convergence behavior. The examination of convergence through the use of eco-efficiency indicators that capture both the environmental and economic effects of production may be more fruitful in contributing to the debate on mitigation strategies and allocation mechanisms.

Article

Xiaoyu Li and Sathya Gopalakrishnan

The convergence of geophysical and economic forces that continuously influence environmental quality in the coastal zone presents a grand challenge for resource and environmental economists. To inform climate adaptation policy and identify pathways to sustainability, economists must draw from different lines of inquiry, including nonmarket valuation, quasi-experimental analyses, common-pool resource theory, and spatial-dynamic modeling of coupled coastal-economic systems. Theoretical and empirical contributions in valuing coastal amenities and risks help examine the economic impact of climate change on coastal communities and provide a key input to inform policy analysis. Co-evolution of community demographics, adaptation decisions, and the physical coastline can result in unintended consequences, like climate-induced migration, that impacts community composition after natural disasters. Positive and normative models of coupled coastline systems conceptualize the feedbacks between physical coastline dynamics and local community decisions as a dynamic geoeconomic resource management problem. There is a pressing need for interdisciplinary research across natural and social sciences to better understand climate adaptation and coastal resilience.

Article

David Wolf and H. Allen Klaiber

The value of a differentiated product is simply the sum of its parts. This concept is easily observed in housing markets where the price of a home is determined by the underlying bundle of attributes that define it and by the price households are willing to pay for each attribute. These prices are referred to as implicit prices because their value is indirectly revealed through the price of another product (typically a home) and are of interest as they reveal the value of goods, such as nearby public amenities, that would otherwise remain unknown. This concept was first formalized into a tractable theoretical framework by Rosen, and is known as the hedonic pricing method. The two-stage hedonic method requires the researcher to map housing attributes into housing price using an equilibrium price function. Information recovered from the first stage is then used to recover inverse demand functions for nonmarket goods in the second stage, which are required for nonmarginal welfare evaluation. Researchers have rarely implemented the second stage, however, due to limited data availability, specification concerns, and the inability to correct for simultaneity bias between price and quality. As policies increasingly seek to deliver large, nonmarginal changes in public goods, the need to estimate the hedonic second stage is becoming more poignant. Greater effort therefore needs to be made to establish a set of best practices within the second stage, many of which can be developed using methods established in the extensive first-stage literature.

Article

The economy of territory that became the United States evolved dramatically from ca. 1000 ce to 1776. Before Europeans arrived, the spread of maize agriculture shifted economic practices in Indigenous communities. The arrival of Europeans, starting with the Spanish in the West Indies in 1492, brought wide-ranging change, including the spread of Old World infectious disease and the arrival of land- and resource-hungry migrants. Europeans, eager to extract material wealth, came to rely on the trade in enslaved Africans to produce profitable crops such as tobacco, rice, and sugar, and they maintained connections with Indigenous communities to sustain the fur trade. The declining number of Indigenous peoples, combined with growing numbers of those of European or African origin, altered the demographic profile of North America, particularly in the territory east of the Mississippi River. Over time, Europeans’ consumer choices expanded, though the wealth gap between white colonists grew, as did the economic gap between free colonists, on the one hand, and unfree Black and Native peoples on the other.

Article

Emissions from greenhouse gases are predicted to cause climate to change. Increased solar radiation gradually warms the oceans, which leads to warmer climates. How much future climates will change depends on the cumulative emissions of greenhouse gases, which in turn depends on the magnitude of future economic growth. The global warming caused by humanmade emissions will likely affect many phenomena across the planet. The future damage from climate change is the net damage that these changes will cause to mankind. Oceans are expected to expand with warmer temperatures, and glaciers and ice sheets are expected to melt, leading to sea level rise over time (a damage). Crops tend to have a hill-shaped relationship with temperature, implying that some farms will be hurt by warming and some farms will gain, depending on their initial temperature. Cooling expenditures are expected to increase (a damage), whereas heating expenditures are expected to fall (a benefit). Water is likely to become scarcer as the demand for water increases with temperature (a damage). Warming is expected to cause ecosystems to migrate poleward. Carbon fertilization is expected to cause forest ecosystems to become more productive, but forest fires are expected to be more frequent so that it is uncertain whether forest biomass will increase or decrease. The expected net effect of all these forest changes is an increase in timber supply (a benefit). It is not known how ecosystem changes will alter overall enjoyment of ecosystems. Warmer summer temperatures will cause health effects from heat waves (a damage), but even larger reductions in health effects from winter cold (a benefit). Large tropical cyclones are expected to get stronger, which will cause more damage from floods and high winds. Winter recreation based on snow will be harmed, but summer outdoor recreation will enjoy a longer season, leading to a net benefit. The net effect of historic climate change over the last century has been beneficial. The beneficial effects of climate change have outweighed the harmful effects across the planet. However, the effects have not been evenly distributed across the planet, with more benefits in the mid to high latitudes and more damage in the low latitudes. The net effect of future climate is expected to turn harmful as benefits will shrink and damages will become more pervasive. A large proportion of the damage from climate change will happen in the low latitudes, where temperatures will be the highest. Measurements of the economic impact of climate change have changed over time. Early studies focused only on the harmful consequences of climate change. Including climate effects that are beneficial has reduced net damage. Early studies assumed no adaptation to climate change. Including adaptation has reduced the net harm from climate change. Catastrophe has been assumed to be a major motivation to do near-term mitigation. However, massive sea level rise, ecosystem collapse, and high climate sensitivity are all slow-moving phenomena that take many centuries to unfold, suggesting a modest present value.

Article

Corporate social responsibility (CSR) refers to the incorporation of environmental, social, and governance (ESG) considerations into corporate management, financial decision-making, and investors’ portfolio decisions. Socially responsible firms are expected to internalize the externalities they create (e.g., pollution) and be accountable to shareholders and other stakeholders (employees, customers, suppliers, local communities, etc.). Rating agencies have developed firm-level measures of ESG performance that are widely used in the literature. However, these ratings show inconsistencies that result from the rating agencies’ preferences, weights of the constituting factors, and rating methodology. CSR also deals with sustainable, responsible, and impact investing. The return implications of investing in the stocks of socially responsible firms include the search for an EGS factor and the performance of SRI funds. SRI funds apply negative screening (exclusion of “sin” industries), positive screening, and activism through engagement or proxy voting. In this context, one wonders whether responsible investors are willing to trade off financial returns with a “moral” dividend (the return given up in exchange for an increase in utility driven by the knowledge that an investment is ethical). Related to the analysis of externalities and the ethical dimension of corporate decisions is the literature on green financing (the financing of environmentally friendly investment projects by means of green bonds) and on how to foster economic decarbonization as climate change affects financial markets and investor behavior.

Article

Integrated assessment models (IAMs) of the climate and economy aim to analyze the impact and efficacy of policies that aim to control climate change, such as carbon taxes and subsidies. A major characteristic of IAMs is that their geophysical sector determines the mean surface temperature increase over the preindustrial level, which in turn determines the damage function. Most of the existing IAMs assume that all of the future information is known. However, there are significant uncertainties in the climate and economic system, including parameter uncertainty, model uncertainty, climate tipping risks, and economic risks. For example, climate sensitivity, a well-known parameter that measures how much the equilibrium temperature will change if the atmospheric carbon concentration doubles, can range from below 1 to more than 10 in the literature. Climate damages are also uncertain. Some researchers assume that climate damages are proportional to instantaneous output, while others assume that climate damages have a more persistent impact on economic growth. The spatial distribution of climate damages is also uncertain. Climate tipping risks represent (nearly) irreversible climate events that may lead to significant changes in the climate system, such as the Greenland ice sheet collapse, while the conditions, probability of tipping, duration, and associated damage are also uncertain. Technological progress in carbon capture and storage, adaptation, renewable energy, and energy efficiency are uncertain as well. Future international cooperation and implementation of international agreements in controlling climate change may vary over time, possibly due to economic risks, natural disasters, or social conflict. In the face of these uncertainties, policy makers have to provide a decision that considers important factors such as risk aversion, inequality aversion, and sustainability of the economy and ecosystem. Solving this problem may require richer and more realistic models than standard IAMs and advanced computational methods. The recent literature has shown that these uncertainties can be incorporated into IAMs and may change optimal climate policies significantly.

Article

The window of opportunity for containing risks of dangerous instability in the global climate system is closing rapidly. The response of the international community is embedded in the 2015 Paris Agreement, signed by 195 parties. Implementing the mitigation pledges parties submitted for the agreement is an important first step, although an additional mechanism to coordinate and scale up mitigation policy at the international level will likely be needed. Carbon taxation, or similar pricing, has a pivotal role, providing across-the-board incentives for reducing emissions and the critical price signal for redirecting investment, but pricing has proved difficult politically. Analytical literature on carbon taxation provides practical guidance on the role of taxation in implementing the Paris Agreement and enhancing its acceptability. Shifting taxes off labor and capital and onto carbon or fossil fuels can produce a “double dividend” by reducing environmental harm and lowering the burden broader taxes impose on the economy. Broader taxes both discourage work effort and investment and promote tax-sheltering behavior (e.g., activity in the informal sector). For various technical and practical reasons, however, it may not make sense to set the carbon tax rate above levels warranted on environmental grounds. The literature emphasizes the general importance of using carbon pricing revenues to benefit the economy, for example, lowering burdensome taxes or funding productive investments. These economic benefits are forgone if instead carbon pricing revenues are given to households in lump-sum dividends. Where higher energy prices are subject to public acceptability constraints, a package of regulations or their fiscal equivalents (known as “feebates”) have an important role in reinforcing carbon pricing. Carbon mitigation can also produce important domestic environmental co-benefits, such as reductions in local air pollution mortality. Unilateral action may be in many countries’ own interests before even counting the global climate benefits. Recent studies have quantified the carbon prices implicit in countries’ Paris mitigation pledges. These implicit prices differ widely across countries with the stringency of pledges and the responsiveness of emissions to pricing, underscoring the potential efficiency gains from some degree of price coordination at the international level. In fact, an international carbon price floor arrangement could be strikingly effective to the extent that it promotes more mitigation in key emerging market economies, such as China and India. The price floor need only cover a handful of large emitters, could be designed equitably with higher requirements for advanced countries, and could be designed flexibly to accommodate different policy approaches at the national level. Domestically, policymakers need to develop comprehensive mitigation strategies, ideally with carbon pricing as the key element. These strategies need to distribute burdens equitably, assist vulnerable groups, and include supporting measures for investment and pricing for broader sources of greenhouse gases.

Article

To guide climate change policymaking, we need to understand how technologies and behaviors should be transformed to avoid dangerous levels of global warming and what the implications of failing to bring forward such transformation might be. Integrated assessment models (IAMs) are computational tools developed by engineers, earth and natural scientists, and economists to provide projections of interconnected human and natural systems under various conditions. These models help researchers to understand possible implications of climate inaction. They evaluate the effects of national and international policies on global emissions and devise optimal emissions trajectories in line with long-term temperature targets and their implications for infrastructure, investment, and behavior. This research highlights the deep interconnection between climate policies and other sustainable development objectives. Evolving and focusing on one or more of these key policy questions, the large family of IAMs includes a wide array of tools that incorporate multiple dimensions and advances from a range of scientific fields.