California dreaming: Why environmental justice is integral to the success of climate change policy.

In the realm of climate policy, issues of environmental justice (EJ) are often treated as second-order affairs compared to overarching sustainability goals. We argue that EJ is in fact critical to successfully addressing our national and global climate challenges; indeed, centering equity amplifies the voices of the diverse constituencies most impacted by climate change and that are needed to build successful coalitions that shape and advance climate change policy. We illustrate this perspective by highlighting the experience of California and the contentious processes by which EJ became integrated into the state's climate action efforts. We examine the achievements and shortcomings of California's commitment to climate justice and discuss how lessons from the Golden State are influencing the evolution of current federal climate change policy.

Assessing the distribution of employment vulnerability to the energy transition using employment carbon footprints.

As the world moves away from fossil fuels, there is growing recognition of the need for a just transition of those working in carbon-intensive industries and for policy to support this transition. While recent policies such as the U.S. Inflation Reduction Act (IRA) have begun to incorporate support for energy-intensive regions, little work has thoroughly investigated which communities are most vulnerable to economic disruption in the energy transition and therefore require policy support. This paper analyzes the distribution of employment vulnerability in the U.S. by calculating the average "employment carbon footprint" of close-to every job in the U.S. economy at high geographic and sectoral granularity. The measure considers employment vulnerability across the entire economy and captures both fossil fuel consumption and production effects, with the sectors covered in our analysis accounting for 86% of total U.S. employment and 94% of U.S. carbon emissions outside of the transportation sector. We find that existing efforts to identify at-risk communities both in the literature and the IRA exclude regions of high employment vulnerability, and thereby risk leaving these communities behind in the energy transition. This work underscores the importance of proactive and continuous measures of employment vulnerability, presents policymakers with much-needed data to incorporate such measures into just transition policy and makes the case for place-based policy approaches when considering how best to support communities through the energy transition.

Air quality equity in US climate policy.

The United States government has indicated a desire to advance environmental justice through climate policy. As fossil fuel combustion produces both conventional pollutants and greenhouse gas (GHG) emissions, climate mitigation strategies may provide an opportunity to address historical inequities in air pollution exposure. To test the impact of climate policy implementation choices on air quality equity, we develop a broad range of GHG reduction scenarios that are each consistent with the US Paris Accord target and model the resulting air pollution changes. Using idealized decision criteria, we show that least cost and income-based emission reductions can exacerbate air pollution disparities for communities of color. With a suite of randomized experiments that facilitates exploration of a wider climate policy decision space, we show that disparities largely persist despite declines in average pollution exposure, but that reducing transportation emissions has the most potential to reduce racial inequities.

Why marginal CO2 emissions are not decreasing for US electricity: Estimates and implications for climate policy.

Marginal emissions of CO2 from the electricity sector are critical for evaluating climate policies that rely on shifts in electricity demand or supply. This paper provides estimates of marginal CO2 emissions from US electricity generation using the most recently available and comprehensive data. The estimates vary by region, hour of the day, and year to year over the last decade. We identify an important and somewhat counterintuitive finding: While average emissions have decreased substantially over the last decade (28% nationally), marginal emissions have increased (7% nationally). We show that underlying these trends is primarily a shift toward greater reliance on coal to satisfy marginal electricity use. We apply our estimates to an analysis of the Biden administration's target of having electric vehicles (EVs) make up 50% of new vehicle purchases by 2030. We find that, without significant and concurrent changes to the electricity sector, the increase in electricity emissions is likely to offset more than half of the emission reductions from having fewer gasoline-powered vehicles on the road. Moreover, using average rather than marginal emissions to predict the impacts significantly overestimates the emission benefits. Overall, we find that the promise of EVs for reducing emissions depends, to a large degree, on complementary policies that decarbonize both average and marginal emissions in the electricity sector.

Focusing climate negotiations on a uniform common commitment can promote cooperation.

International cooperation on the reduction of greenhouse gas emissions, disarmament, or free trade needs to be negotiated. The success of such negotiations depends on how they are designed. In the context of international climate change policy, it has been proposed [e.g., M. L. Weitzman J. Assoc. Environ. Resour. Econ. 1, 29-49 (2014)] that shifting the negotiation focus to a uniform common commitment (such as a uniform minimum carbon price) would lead to more ambitious cooperation. Yet, a proof-of-concept for this important claim is lacking. Based on game theoretical analyses, we present experimental evidence that strongly supports this conjecture. In our study, human subjects negotiate contributions to a public good. Subjects differ in their benefits and costs of cooperation. Participation in the negotiations and all commitments are voluntary. We consider treatments in which agreements are enforceable, and treatments in which they have to be self-enforcing. In both situations, negotiating a uniform common commitment is more successful in promoting cooperation than negotiating individual commitments (as in the Paris Agreement) and complex common commitments that tailor the commitment to the specific situation of each party (as attempted with the Kyoto Protocol). Furthermore, as suggested by our model, a uniform common commitment benefits most from being enforced.

Addressing partial identification in climate modeling and policy analysis.

Numerical simulations of the global climate system provide inputs to integrated assessment modeling for estimating the impacts of greenhouse gas mitigation and other policies to address global climate change. While essential tools for this purpose, computational climate models are subject to considerable uncertainty, including intermodel "structural" uncertainty. Structural uncertainty analysis has emphasized simple or weighted averaging of the outputs of multimodel ensembles, sometimes with subjective Bayesian assignment of probabilities across models. However, choosing appropriate weights is problematic. To use climate simulations in integrated assessment, we propose, instead, framing climate model uncertainty as a problem of partial identification, or "deep" uncertainty. This terminology refers to situations in which the underlying mechanisms, dynamics, or laws governing a system are not completely known and cannot be credibly modeled definitively even in the absence of data limitations in a statistical sense. We propose the min-max regret (MMR) decision criterion to account for deep climate uncertainty in integrated assessment without weighting climate model forecasts. We develop a theoretical framework for cost-benefit analysis of climate policy based on MMR, and apply it computationally with a simple integrated assessment model. We suggest avenues for further research.

Climate risk and green total factor productivity in agriculture: The moderating role of climate policy uncertainty.

In light of the escalating global warming and the escalating frequency of extreme weather events, the agricultural sector, being a fundamental and pivotal industry worldwide, is encountering substantial challenges due to climate change. Using Chinese provincial panel data for 2000-2021, this paper utilizes a two-way fixed-effect model to investigate the impact of Climate Risk (CR) on green total factor productivity in agriculture (AGTFP), with China's climate policy uncertainty (CPU) being introduced as a moderating variable within the research framework to scrutinize its influence in this context. The findings reveal a noteworthy adverse effect of CR on AGTFP, further exacerbated by CPU. Heterogeneity analysis results show that there is a clear regional variation in the effect of CR on AGTFP across different Chinese regions, with CR significantly inhibiting AGTFP development in the northern regions and provinces in major grain producing regions. Consequently, there is a pressing necessity to bolster the establishment of climate change monitoring infrastructures, devise tailored climate adaptation strategies at a regional level, and enhance the clarity and predictability of climate policies to fortify the resilience and sustainability of agricultural production systems.

The risk paradox: Exploring asymmetric nexus between climate policy uncertainty and renewable energy technology budgets.

The ups and downs of climate policy uncertainty (CPU) cast a captivating shadow over the budgets allocated to renewable energy (RE) technologies, where strategic choices and risk assessment will determine the course of our green environmental revolution. The main intention of this investigation is to scrutinize the effect of CPU on the RE technology budgets (RETBs) in the top 10 countries with the highest RE research and development budgets (the USA, China, South Korea, India, Germany, the United Kingdom, France, Japan, Australia, and Italy). Although former researchers have typically employed panel data tools to contemplate the connection between CPU and RE technology, they repeatedly ignored variations in this connection throughout different economies. In contrast, our research adopts a unique approach, "quantile-on-quantile," to check this association at the country-to-country level. This approach offers a comprehensive worldwide perspective while procuring tailor-made perceptions for individual economies. The outcomes suggest that CPU significantly decreases RETBs across several data quantiles in our sample nations. In addition, the outcomes underscore that the connections between our variables differ among nations. These outcomes highlight the significance of policymakers implementing thorough appraisals and skillfully governing plans relevant to CPU and RETBs.

The dark side of climate policy uncertainty: Hindering energy transition by shaping environmental taxes effectiveness.

Climate policy uncertainty (CPU) may have an adverse impact on the environment by interfering with the effectiveness of environmental policies, but there is currently little evidence to support this indirect effect. By incorporating CPU into the transition function, this paper utilizes the panel smooth transition regression (PSTR) to dynamically analyze how CPU affects the relationship between environmental taxes (ETR) and energy transition. When CPU exceeds the threshold, the promoting effect of ETR on energy transition weakens or reverses. The robustness of the main conclusions is demonstrated by establishing a PSTR estimator with the instrumental variable. This paper also constructs a counterfactual scenario, showing that CPU reduces the positive impact of ETR on renewable energy consumption and generation by 7.6% and 3.5%, respectively. Further analysis indicates that this negative effect arises because CPU likely increases investment risk, particularly for long-term green projects, thereby inhibiting the clean energy market and energy-related green technological innovation. Heterogeneity analysis find that the weakening effect of CPU on the effectiveness of ETR is stronger in countries with low energy resource endowment, high energy intensity, and lower economic development levels, underscoring the need for tailored policy approaches. This research emphasizes that for countries with ambitious energy transition goals, climate policy stability is crucial for ensuring the healthy development of environmental taxes policy and renewable energy markets.

Size of government and willingness-to-pay for environmental policy: Evidence from a cross-country survey.

Citizen support is an important precursor to climate change mitigation polices. Public opinion can shape public policy and vice versa. This paper uses the 2010 International Social Survey Programme (ISSP) Environment Module to investigate cross-national differences in support for climate policy. We introduce size of government, measured by government revenues as a share of GDP, as a new country-level factor. Our sample includes 31,511 responses from 33 countries. We use multilevel models to estimate the relationship between country-level factors and environmental policy support, conditional on a series of individual factors. Increasing the size of government by one standard deviation reduces support for environmental policy by 0.13 points on a 5-point scale. For comparison, a one standard deviation increase in GDP per capita leads to a 0.24 increase in support and a one standard deviation increase in air pollution leads to a 0.13 point increase. The implication for environmental policy is that high tax countries have an aversion to price and tax increases aimed at protecting the environment. We conclude that use of taxes for environmental policy must include clear expectations for how revenues will be recycled or how other taxes will be lowered if they are to gain widespread support.

Energy transition in China: Is there a role for climate policy uncertainty?

In exploring strategies to address global climate change and achieve carbon neutrality goals, climate policy uncertainty (CPU) has become a significant challenge that cannot be ignored. It is crucial to understand how regions can effectively respond to climate risks and achieve energy transition. In this context, we utilized panel data from 277 Chinese cities and employed fixed effects models to analyze the relationship and mechanism between Chinese climate policy uncertainty (CCPU) and energy transition (ET). Our study found that CCPU significantly hinders the progress of ET, and this impact exhibits asymmetric characteristics. Compared to regions with strong environmental regulations, limited fiscal decentralization, and higher administrative levels, CCPU has a more pronounced inhibitory effect on ET in regions with weak environmental regulations, significant fiscal decentralization, and lower administrative levels. Green finance and energy structure are identified as important channels through which CCPU reduces ET. Additionally, further analysis indicates that CCPU significantly suppresses regional high-quality economic development and innovation, and this impact is achieved through inhibiting ET. Therefore, in the face of external uncertainties, this research can provide insights for local climate policy formulation. Focusing on and striving to reduce CCPU will contribute to the development of ET in regions.

Climate policy uncertainty and its impact on the volatility, correlation, and hedging of sovereign and green bonds.

This study develops monthly climate policy uncertainty (CPU) indexes for 21 economies and three global CPU indexes to evaluate their effects on the long-term sovereign and green bond volatilities and the long-term sovereign-green bond correlation. Findings show significant increases in the CPU indexes during key climate policy events. Using the extended GARCH-MIDAS-CPU and DCC-MIDAS-CPU models, it finds that CPU significantly affects sovereign bond volatility following the Paris Agreement. Green bonds are more effective as hedging tools in the sovereign bond markets of emerging markets and developing economies than in developed ones, especially post-Paris Agreement. Diversified portfolios including green bonds offer superior hedging effectiveness. This study highlights the pivotal role of green bonds in managing CPU and promoting sustainable development goals.

Financial regulations and sustainability: The role of energy price and climate policy uncertainty.

Russia ranks among the top five countries worldwide in terms of carbon emissions, with the energy, transportation, and manufacturing sectors as the major contributors. This poses a significant threat to both current and future generations. Russia faces challenges in achieving Sustainable Development Goal 13, necessitating the implementation of more innovative policies to promote environmental sustainability. Considering this alarming situation, this study investigates the role of financial regulations, energy price uncertainty, and climate policy uncertainty in reshaping sectoral CO2 emissions in Russia. This study utilizes a time-varying bootstrap rolling-window causality (BRW) approach using quarterly data from 1990 to 2021. The stability test for parameters indicates instability, suggesting that the full sample causality test may yield incorrect inferences. Thus, the BRW approach is employed for valid inferences. Our findings confirm the time-varying negative impact of financial regulations on CO2 emissions from energy, manufacturing, and transportation sectors. Additionally, findings confirm time-varying positive impact of energy prices and climate policy uncertainty on CO2 emissions from the energy, manufacturing, and transportation sectors. Strong financial regulations and stable energy and climate policies are crucial for achieving sustainability, highlighting significant policy implications for policymakers and stakeholders.

The impact of climate policy uncertainty on corporate pollution Emissions--Evidence from China.

Climate change is considered one of the major systemic risks facing the world in the 21st century. To address climate change, China has adopted a series of climate policies, but the uncertainty brought about by frequent climate policy issuance has increased pressure on enterprises, which may not be conducive to enterprises reducing emissions. This paper uses data on 1211 listed companies on the A-share market in China from 2012 to 2022 to study the impact of climate policy uncertainty on enterprise pollutant emissions. The research findings show that climate policy uncertainty increases corporate pollution emissions; climate policy uncertainty mainly generates negative impacts on enterprise environmental regulation, social responsibility, and R&D investment, thereby negatively affecting enterprise emissions reduction. Further heterogeneity analysis shows that climate policy uncertainty in China has a more significant impact on non-state-owned enterprises, technology-intensive enterprises, lightly polluting enterprises, and enterprises in western regions. These findings emphasize the importance of enterprise social responsibility, environmental regulation, and R&D investment in enterprise emissions reduction and provide policy implications for Chinese enterprises to optimize their energy-saving and emission reduction strategies in the face of climate policy uncertainty.

The impact of climate policy uncertainty on China's green energy and non-ferrous metals market co-movement: Evidence from spillover perspectives.

The rapid development of green energy would render a profound impact on the non-ferrous metals markets in China. This paper adopts the quantile vector autoregression (QVAR) to investigate the spillover effects between China's green energy and non-ferrous metals markets as well as their dynamic pattern under normal and extreme conditions. Furthermore, GARCH-MIDAS model and quantile regression method are applied to examine the impact of China's climate policy uncertainty on the spillovers between the two markets. In doing so, we find that green energy markets mainly act as transmitters of return spillover effects to non-ferrous metals markets during normal market times and periods of downturns. However, in upturns, the non-ferrous metals markets would easily transit spillover effects to green energy ones. It is further indicated that China's climate policy uncertainty exacerbates the spillover effect, and the exacerbated effect of high uncertainty on the market relationship when the spillover effect is at high level is the most significant.

Asymmetric impacts of climate policy uncertainty, investor sentiment on energy prices and renewable energy consumption: Evidence from NARDL and wavelet coherence.

This paper seeks to look into the asymmetric impacts posed by climate policy uncertainty (CPU) and investor sentiment (IS) upon the price of non-renewable energy, specifically natural gas prices, and the consumption of renewable energy, embodied in geothermal energy, biofuels, and fuel ethanol. To this end, the analysis draws on a non-linear autoregressive distributed lag (NARDL) model and wavelet coherence (WTC) technique with monthly data from January 2000 to December 2021. The NARDL results establish an asymmetric association between the variables, where negative shocks to CPU exert a greater effect on each energy variable than positive shocks, while the reverse is true for IS. Furthermore, it has been noticed that CPU and IS exhibit primarily negative correlations with the target variables over the long term, with CPU having a more pronounced effect on natural gas prices than on other forms of renewable energy consumption. Wavelet analysis also reveals that CPU leads the energy variables over the medium to long run, while IS assumes a dominant role in the short to medium run. These momentous findings underscore the importance of this study in informing energy policy formulation and environmental management, as well as optimizing investor portfolios.

Does climate policy uncertainty drive the extreme spillovers of carbon-energy-shipping markets?

In addressing the ramifications of climate change, the shipping industry, reliant on energy, has been integrated into the Emissions Trading System (ETS). This study utilizes the quantile connectedness model to investigate the information spillover mechanisms and extreme time-varying interconnections among carbon, energy, and shipping markets. Whether climate policy uncertainty drives the extreme interconnections is also discussed during both pre- and post-Paris Agreement periods, by using GARCH-MIDAS model. The empirical findings underscore the following key points: (i) the systemic connectedness is highly sensitive to market conditions and major events, increasing significantly under extreme market conditions; (ii) following the implementation of the Paris Agreement, an elevated level of informational interdependence has manifested between the carbon market and the energy and shipping sectors; (iii) the information transfer mechanism between carbon and shipping sectors creates direct and indirect spillover paths, with crude oil market mediating the indirect path; (iv) climate policy uncertainty greatly affects the extreme time-varying interconnections, and this impact has decreased after the Paris Agreement came into effect. These results offer valuable insights for market policymakers and shipping companies in achieving a balance between carbon emission reduction and shipping business, particularly amidst heightened climate policy uncertainty.

Least-cost decarbonization pathways for electricity generation in Finland: A convex quantile regression approach.

This study investigates the least-cost decarbonization pathways in the Finnish electricity generation industry in order to achieve the national carbon neutrality goal by 2035. Various abatement measures, such as downscaling production, capital investment, and increasing labor and intermediate inputs, are considered. The marginal abatement costs (MACs) of greenhouse gas emissions are estimated using the convex quantile regression method and applied to unique register-based firm-level greenhouse gas emission data merged with financial statement data. We adjust the MAC estimates for the sample selection bias caused by zero-emission firms by applying the two-stage Heckman correction. Our empirical findings reveal that the median MAC ranges from 0.1 to 3.5 euros per tonne of CO2 equivalent. The projected economic cost of a 90% reduction in emissions is 62 million euros, while the estimated cost of achieving zero emissions is 83 million euros.

China's anthropogenic N2O emissions with analysis of economic costs and social benefits from reductions in 2022.

We assess China's overall anthropogenic N2O emissions via the official guidebook published by Chinese government. Results show that China's overall anthropogenic N2O emissions in 2022 were around 1593.1 (1508.7-1680.7) GgN, about 47.0 %, 27.0 %, 13.4 %, 4.9 %, and 7.7 % of which were caused by agriculture, industry, energy utilization, wastewater, and indirect sources, respectively. Maximum reduction rate for N2O emissions from agriculture, industry, energy utilization, wastewater, and indirect sources can achieve 69 %, 99 %, 79 %, 86 %, and 48 %, respectively, in 2022. However, given current global scenarios with a rapidly changing population and geopolitical and energy tension, the emission reduction may not be fully fulfilled. Without compromising yields, China's theoretical minimum anthropogenic N2O emissions would be 600.6 (568.8-633.6) GgN. In terms of the economic costs for reducing one kg of N2O-N emissions, the price ranged from €12.9 to €81.1 for agriculture, from €0.08 to €0.16 for industry, and from €104.8 to €1571.5 for energy utilization. We acknowledge the emission reduction rates may not be completely realistic for large-scale application in China. The social benefits gained from reducing one kg of N2O-N emissions in China was about €5.2, indicating anthropogenic N2O emissions caused a loss 0.03 % of China's GDP, but only justifying reduction in industrial N2O emissions from the economic perspective. We perceive that the present monetized values will be trustworthy for at least three to five years, but later the numerical monetized values need to be considered in inflation and other currency-dependent conditions.

Quantile connectedness between the climate policy and economic uncertainty: Evidence from the G7 countries.

In this study, we investigate the transmission mechanism between climate policy uncertainty (CPU) and economic policy uncertainty (EPU) in the G7 countries. To account for different conditions, we use a quantile-based VAR (Q-VAR) model over the period between 2000 and 2021. Our results show high connectedness between the CPU and the EPU of G7 countries, particularly at extreme quantile orders. On the other hand, the spillover effects between climate and economic policy uncertainty differ depending on the distributional levels of the uncertainty indices. The CPU is a net receiver of uncertainty shocks, while for almost all countries, the EPU acts as a net receiver or emitter, depending on the economic situation. During times of high or low economic uncertainty, the EPU of all G7 countries is strongly affected by shocks originating from the CPU. Moreover, the results indicate that the dynamic spillover patterns between EPU and CPU vary over time, responding to different economic events and financial crises. These results call for policymakers and governments to urgently integrate climate considerations into economic planning, fiscal policies, and regulatory frameworks to promote sustainable economic growth and mitigate the impacts of climate change.