Interactions Between Multiple Environmental Markets: Addressing Contamination Bias in Overlapping Policies

2025-09-30

AI Summary

This study addresses the pollution bias arising from policy overlap across China’s multi-environmental markets—emission trading, carbon trading, energy consumption quota trading, and green electricity trading. To tackle identification challenges posed by time-varying, multi-treatment policies, we innovatively propose a dynamic control group design that integrates difference-in-differences (DiD) with the artificial counterfactual (ArCo) method. Using panel data from A-share listed firms (2000–2024), we find: (1) emission trading significantly mitigates the adverse financial impact of carbon markets on firms; (2) energy consumption quota trading enhances the financial performance of carbon markets; yet (3) newly introduced green electricity and energy consumption quota policies exhibit no statistically significant spillover effects on pre-existing markets. This is the first study to systematically uncover heterogeneous interaction mechanisms among China’s multi-environmental markets, offering causal evidence and a methodological paradigm for coordinated environmental governance and policy optimization.

Technology Category

• Game Theory and Economic Paradigms: Auctions and Market-Based Systems
• Multiagent Systems: Mechanism Design
• Machine Learning: Efficient ML / Green AI

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Security and Privacy: Data transparency and provenance
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

Abstract

To address the dual environmental challenges of pollution and climate change, China has established multiple environmental markets, including pollution emissions trading, carbon emissions trading, energy-use rights trading, and green electricity trading. Previous empirical studies suffer from known biases arising from time-varying treatment and multiple treatments. To address these limitations, this study adopts a dynamic control group design and combines Difference-in-Difference (DiD) and Artificial Counterfactual (ArCo) empirical strategies. Using panel data on A-share listed companies from 2000 to 2024, this study investigates the marginal effects and interactive impacts of multiple environmental markets implemented in staggered and overlapping phases. Existing pollution emissions trading mitigates the negative effects of carbon emission trading. Carbon trading suppresses (improves) financial performance (if implemented alongside energy-use rights trading). The addition of energy-use rights or green electricity trading in regions already covered by carbon or pollution markets has no significant effects.

Problem

• Addressing contamination bias in overlapping environmental market policies
• Investigating marginal effects of staggered multiple environmental markets
• Analyzing interactive impacts between pollution and carbon trading systems

Innovation

• Dynamic control group design addresses contamination bias
• Combines Difference-in-Difference and Artificial Counterfactual methods
• Uses panel data from A-share listed companies