Harvesting Green Economy: Exploring the Impact of New Energy Demonstration City Policy on China’s Urban Green Total Factor Productivity
Bibliographic record
Abstract
To assess the applicability of the “Porter hypothesis” (PH) within the context of China’s energy policies, enhance theoretical understanding of environmental regulation, and explore the green economy effect of the new energy demonstration city policy (NEDC), this study employs a difference-in-differences (DID) model. Based on panel data from 284 prefecture-level cities in China from 2007 to 2022, it empirically examines the impact and underlying mechanisms of the NEDC on urban green total factor productivity (GTFP). The main findings are as follows: (1) The NEDC significantly increased urban GTFP by 2.3%. This conclusion remains robust even after a series of robustness and endogeneity tests, including alternative explained variable, winsorization analysis, placebo tests, propensity score matching-DID, and instrumental variable, among other approaches. These findings provide strong empirical support for the PH in the context of China’s environmental and energy policy landscape. (2) Mechanism analysis reveals that the policy promotes urban GTFP growth primarily through four channels: increasing government attention to environmental governance, advancing industrial structure upgrading, improving energy efficiency, and stimulating technological innovation capacity. These findings provide concrete pathways for achieving green economic development. (3) Heterogeneity analysis shows that the green economic effects of the NEDC are more pronounced in cities characterized by higher levels of industrial agglomeration, those located in the eastern region, and resource-based cities. This highlights the importance of place-based and targeted policy implementation, offering empirical evidence for differentiated policy design, and precise governance. Therefore, by showcasing the successful experience of NEDC, this study provides valuable insights and policy implications for other countries pursuing energy transitions and sustainable development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".