Economic Determinants of Renewable Energy Consumption in China: Integrating Green Infrastructure, Urban Agglomeration, and Environmental Stressors
Bibliographic record
Abstract
ABSTRACT China's rapid economic growth has intensified pressure on its energy system, with persistent fossil fuel dependence driving severe air pollution and escalating carbon emissions. Accelerating the transition toward renewable energy is thus vital for achieving the nation's “dual carbon” goals. This study investigates the structural and environmental determinants of renewable energy consumption (REC) in China from 2005 to 2023 using a multi‐method econometric framework. By integrating green infrastructure (forest area), energy efficiency (energy intensity), environmental stressors (PM 2.5 exposure), and spatial dynamics (urban agglomerations exceeding one million inhabitants), the analysis offers a comprehensive understanding of renewable energy drivers. Employing the autoregressive distributed lag (ARDL) bounds approach, Dynamic OLS (DOLS) for long‐run robustness, and Toda–Yamamoto causality tests for directionality, the results confirm strong long‐run cointegration between REC and its determinants. Forest cover, urban agglomerations, and PM 2.5 exposure significantly influence renewable energy demand, while energy intensity reflects structural breaks aligned with policy reforms in the late 2000s. These findings highlight that renewable energy uptake in China is shaped by the interplay of ecological resilience, urban transformation, and environmental pressures. The study contributes to sustainable development research by emphasizing that renewable energy policy must be integrated with ecological management, urban planning, and pollution control. Policy recommendations advocate for afforestation, renewable‐integrated urban infrastructure, stricter emission regulation, and ongoing efficiency improvements to accelerate China's clean energy transition and inform broader sustainable development pathways in emerging economies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".