【直播】第四届环境经济学前沿论坛：环境经济学+X

会议议程(拟）

AGENDA

Abstract:

Our study examines the intricate effects of climate change, trade dynamics, and water resources on global economic outcomes. Leveraging high-resolution satellite data, we create a detailed mapping from local climate shocks to country-level agricultural productivity. We then employ a structural trade model following Eaton and Kortum (2002) and explore the welfare implications of precipitation shocks across various scenarios through their impact on the agricultural sector. Our findings highlight significant heterogeneity in local productivity responses to precipitation, and the impact of precipitations can propagate via trade networks and production linkages. We demonstrate that trade and production networks can mitigate the impact of local precipitation shocks, though these effects may spill over into other sectors and regions through market interactions.

Abstract:

The speed at which electricity generation can transition to green energy sources depends in part on the incentives of coal and natural gas plants to enter or exit. I examine how the design of government subsidies and the costs of renewables shape those strategies. To do so, I formulate a nonstationary dynamic model of generator entry and exit that incorporates heterogeneity in entry costs and nests it within a dynamic, hourly model of competition in the wholesale electricity market. I estimate the model using data from Texas. I find that renewable subsidies in place in 2005--20 reduce cumulative CO2 emissions by 1.71 billion tons through 2060, largely because of a dynamic mechanism: they shift expectations about future competition and thereby reduce the entry of new coal plants very early in the transition. I further show that, by leveraging the dynamic mechanism, a short-horizon subsidy can more effectively reduce carbon emissions with less tax burden by bunching more wind investment and intensifying competition earlier for coal power plants.

Abstract:

The inter-temporal resource allocation efficiency of a rights-based common-pool resource management system is threatened by a stock externality when one user’s extraction lowers the resource stock and raises the extraction cost for others. This paper proposes a novel decentralized rights-based allocation mechanism (DRAM) to restore the efficiency loss. DRAM includes two stages. In a voting stage, agents collectively determine a binding extraction target for each period via weighted majority voting; in a market stage, agents trade extraction rights assigned to them within each period. We build a theoretical model to illustrate the efficiency loss from a standard property rights market and demonstrate that DRAM can implement the socially optimal allocation under mild conditions. Laboratory experiments confirm that DRAM outperforms the standard property rights market in aggregate economic efficiency.

Abstract:

Enforcement agencies often struggle with limited resources and inefficient allocation, leading to low detection rates of violations. This study develops and validates a machine learning model using nearly 300,000 environmental inspection records and diverse data sources from Jiangsu, China, to predict environmental violations. Our results show that the machine learning model enhances the detection of violators by 67% without requiring additional resources, or reduces inspection resources by approximately 40% while maintaining the same level of violator identification. Additionally, we propose an innovative enforcement scheme incorporating a “whitelist” to filter out likely compliant facilities and demonstrate its superiority over existing strategies through simulation. These findings illustrate the potential of machine learning and big data to optimize regulatory practices on a broader scale.

Abstract:

Forest carbon offset projects are important sources of carbon removal, yet their integrity has been challenged, as some major carbon-crediting programs have been exposed to over-crediting critics. In this study, we aim to evaluate the integrity of afforestation and reforestation (AR) and improved forest management (IFM) projects under six mainstream carbon-crediting programs, namely, the Clean Development Mechanism (CDM), Verified Carbon Standard (VCS), Gold Standard (GS), American Carbon Registry (ACR), Climate Action Reserve (CAR), and Chinese Certificated Emissions Reduction (CCER) programs, by comparing estimated emissions reductions based on remote sensing data to those claimed by project developers. While we initially identified 682 projects, the availability of public project information limited our evaluation to 214 projects under CDM, VCS, ACR, and CAR programs, spanning 35 countries. Overall, we find risks of over-crediting: on average, claimed credits are significantly greater than satellite-based estimates of emissions reductions. Among the four programs, the IFM projects under the CAR program have the least biases. We find that adopting methodologies for large-scale projects, hiring verifiers with a narrower methodology or program coverage, more developed host countries, and favourable business environments are correlated with less overestimation. Our evaluation calls for more efforts to track and improve the integrity of forest carbon offset projects, thus enhancing the confidence of investment in natural carbon removal solutions.

Abstract:

The Paris Agreement includes a ratchet-up mechanism designed to accelerate the progress towards long-term climate goals by prescribing that parties' commitments to climate change cannot decrease over time. However, this mechanism often leads to reduced early contributions to avoid high obligations later on, known as the ratchet effect. We experimentally investigate this effect in a threshold public goods game and test two amendments: the strong ratchet-up mechanism (SR) and the collective minimum contribution mechanism (CMC), with the former requiring strictly increasing contributions and the latter a collectively chosen minimum. Our experiment supports a significant ratchet effect in the weak ratchet-up mechanism. Subjects reduce their contributions initially, but the increasing contributions over the course of the game are insufficient to offset the early drop, leading to a lower provision rate. The SR mechanism also suffers initial low contributions but recovers faster, effectively compensating the initial drop. The CMC mechanism mitigates the ratchet effect by boosting contributions at the beginning of the game, especially when the minimum contributions are binding. However, both the SR and CMC mechanisms lower individual willingness to contribute. Balancing these conflicting incentives is crucial for an effective climate policy design.

Abstract:

Firms adopt different production modes to manufacture products for domestic and foreign markets, resulting in different carbon emission behaviors for the two production lines. In addition to the reduction effect of technology adoption, we propose the increment effect of higher safety standard compliance on exporting firms' emissions. We develop a simple theoretical model that allows exporting firms to endogenously choose the optimal production mode given foreign consumers' preferences. We investigate the empirical behavior of firm exports and carbon emission based on Chinese exporting firms' data over 2007--2015, using a semiparametric varying coefficient model to allow for the heterogeneous estimation results. We find that the impact of the export share on carbon emission intensity is positive for both the smallest firms and the largest firms, while negative for those in between. The mechanism checks show that the emission elasticity estimates drop down with firm productivity while raising up with capital intensity. Our empirical analysis found strong evidence of carbon heterogeneity. The empirical results explain the reasons for the contradictory conclusions on firm exports and emissions in the literature. Our policy counterfactuals suggest subsidizing small firms while taxing large firms to improve energy efficiency and protect the environment.

Abstract:

Addressing air pollution is a pressing issue. Traditional air pollution regulations primarily target areas with the highest pollution levels. However, the regions with the highest pollution levels may not always be the primary contributors to nationwide pollution due to the long-distance travel of air pollutants. This paper employs high-frequency backward trajectory data to investigate the spillover effects of PM2.5 pollution in China from 2015 to 2023. The city-pair regression reveals a statistically significant positive correlation between the pollution levels in source and destination cities. Notably, the geographic distribution of air pollution spillover impact significantly diverges from the pattern of PM2.5 concentration, indicating a discrepancy between local air pollution levels and national air pollution contributions. Further, the study demonstrates that China’s past city-level PM2.5 reduction targets were primarily determined by PM2.5 concentration rather than the contribution of local air pollution to national air pollution, suggesting a potential loss in efficiency. Lastly, the paper proposes an alternative pollution reduction target allocation scheme that considers spillover effects, ensuring that the marginal cost of pollution reduction in any city yields uniform national benefits. The results suggest that China has disproportionately focused on the most polluted cities while under-reacting to many less-polluted cities with significant spillover impacts.

Abstract:

The Great Recession in 2008 induced a large number of migrants to return to their home city in China.  This paper studies how return migration reshapes environmental preferences and awareness in migrants’ home regions. We employ a shift-share IV based on exogenous changes in world import demand, which predict return migration in the aftermath of the great recession. We show that the exposure to return migration coming from regions featuring stronger environmental awareness increases local people’s environmental preferences. In particular, local people express higher awareness of local pollution issues, take more environment protection actions, and acquire more environmental knowledge. Nevertheless, the effects are different if return migrants are from regions with lower environmental awareness. The effects of inter-regional diffusion of environmental attitudes are stronger if similar dialects are spoken in these regions.

Abstract:

Agriculture accounts for roughly 30% of global carbon emissions, and genetically modified (GM) crops are expected to help reduce carbon emissions in agriculture by using fewer high-emission inputs. However, when examining the gradual roll-out of GM crops across countries, we find that GM crops have increased total agricultural carbon emissions by 7.3\% and increased the carbon-emission intensity of the cropping sector by 9.86\%. The major reason for this unexpected impact is that GM crops have increased crop yield, increased multiple-cropping, crowded out other crops, reduced tree-cover areas, and extended agriculture to marginal lands, and all these changes have increased fertilizer and energy inputs. A potential way to offset the global carbon-emissions effect of GM crops is to export the additional crop outputs to countries with a higher agricultural carbon-emission intensity. However, data show that a large share of GM crops are used for domestic livestock farming, further increasing global carbon emissions.

Abstract:

This paper introduces a framework for measuring sustainable growth that accounts for the limited substitutability between economic and environmental growth, which is often overlooked by existing measures like GDP and other growth indices. Our framework integrates balance information of growth as- pects with aggregate growth levels. Applying this to China’s provinces and pre- fectures from 1985 to 2018 reveals a widening gap between sustainable growth (SG) and GDP due to increasing imbalances among growth aspects. Economic growth dominates while environmental growth declines. The SG-to-GDP ratio follows an inverted U shape, indicating limited substitutability, especially as the economy expands beyond a certain threshold. Large heterogeneity exists within China. Provinces with a dominant service sector show the largest increase in SG per capita from 1985 to 2018 than other provinces. This paper highlights the importance of structural balances in sustainable development.

Abstract:

We investigate how board members’ unexpected flood experiences (during both business and personal trips) affect their firm’s ESG performance. Leveraging anonymized individual-level big data of travel records including business and personal travels from China during 2017--2022, we identify whether board members of publicly listed companies unexpectedly experienced flood events at their destination during their travels. Utilizing a staggered difference-in-differences (DID) method, we find that board flood experiences during business and personal trips increases their firm’s ESG performance 2.43% and 4.03% eight quarters after the flood experiences, respectively, compared to firms whose board members do not experience a flood. The effects are particularly notable when the flood event is extremely severe or the affected board member holds a higher ownership stake in the company. We observe greater effects for larger firms and firms with lower capital costs and default probability. We provide evidence of the “value” mechanism, by showing that this improvement is correlated with business operations and the firm’s exposure to flood shocks. We also provide evidence of the “values” reflecting personal managerial preferences, by showing that the ESG improvement after flood experiences in personal trips is correlated with the inherent personal characteristics of the affected board member.

Abstract:

How trade policy and energy policy interplay in shaping the carbon effect of trade activities is critical to understanding the trade-carbon linkage. Using US-China trade friction as the exogenous shock, we empirically and quantitatively examine the impact of US-China trade friction on carbon emissions, carbon emission intensity, and welfare. With comprehensive firm-level tax survey data, we find that US-China trade friction raises carbon emission intensity at both the city level and firm level. Two mechanisms drive the effects: cities react to the shock by loosening government environmental regulations, which boosts firms usage of coal and then increases subsequent carbon emission intensity; resource reallocation occurs after the shock as former exporters gravitate toward domestic production, which is apparently less clean. Our quantitative analyses further discuss the impact of the trade friction on the welfare of China, the U.S., and the world due to affected production and increased carbon emissions.

Abstract:

A key challenge global society faces is how to engage the public on climate risk and educate individuals on their collective responsibility as consumers, workers, investors, and citizens, including rewarding political leaders for taking regulatory action. Here we test the hypothesis that introducing personalized feedback on the environmental impacts of a consumer’s frequent choices in a specific domain—urban mobility—can lower the carbon-intensity of those choices and spill over to pro-environmental beliefs and attitudes more generally. We recruited 330 university students into a semester-long randomized control trial. We tracked urban mobility choices and, for conditions other than a control, we mapped these choices to personal carbon scores and messaged participants on the environmental co-benefits of taking public transport, including reduced carbon emissions, improved air quality, and active mobility. Participants who stood to receive rewards for low-carbon scores over reference periods of three weeks shifted their choices the most, away from single-rider car travel. Participants did not object to receiving personal carbon scores, likely due to their exposure to positive and hopeful messaging on the importance of taking climate action. We find evidence that the urban mobility intervention, in its strongest form of offering low-carbon rewards on top of tracking personal carbon scores and environmental messaging, shifted stated climate change beliefs and attitudes more generally, i.e., not only specific to the urban mobility domain. This research paves the way for a scalable fintech solution that integrates personal carbon tracking with real-time transportation choices, empowering climate leaders.