How does green finance affect carbon emission intensity? The role of green technology innovation and internet development

International Review of Economics & Finance, Год журнала: 2025, Номер unknown, С. 103995 - 103995

Опубликована: Фев. 1, 2025

Язык: Английский

---

How does new urbanization affect urban carbon emissions? Evidence based on spatial spillover effects and mechanism tests

Urban Climate, Год журнала: 2024, Номер 56, С. 102060 - 102060

Опубликована: Июль 1, 2024

Язык: Английский

---

Dynamic spatiotemporal evolution and spatial effect of carbon emissions in urban agglomerations based on nighttime light data

Sustainable Cities and Society, Год журнала: 2024, Номер 113, С. 105712 - 105712

Опубликована: Июль 27, 2024

Язык: Английский

---

Spatial effects of green innovation and carbon emission reduction in China: Mediating role of infrastructure and informatization

Sustainable Cities and Society, Год журнала: 2024, Номер 106, С. 105426 - 105426

Опубликована: Апрель 9, 2024

Язык: Английский

---

Does environmental decentralization improve industrial ecology? Evidence from China's Yangtze River Economic Belt

Economic Analysis and Policy, Год журнала: 2024, Номер 82, С. 1250 - 1270

Опубликована: Май 19, 2024

Язык: Английский

---

Evaluation of carbon emission efficiency and reduction potential of 336 cities in China

Journal of Cleaner Production, Год журнала: 2023, Номер 428, С. 139372 - 139372

Опубликована: Окт. 17, 2023

Язык: Английский

---

Spatial-temporal multi-factor decomposition and two-dimensional decoupling analysis of China's carbon emissions: From the perspective of whole process governance

Environmental Impact Assessment Review, Год журнала: 2023, Номер 103, С. 107291 - 107291

Опубликована: Сен. 25, 2023

Язык: Английский

---

A novel intervention effect-based quadratic time-varying nonlinear discrete grey model for forecasting carbon emissions intensity

Information Sciences, Год журнала: 2024, Номер 675, С. 120711 - 120711

Опубликована: Май 7, 2024

In the context of severe global warming, accurately exploring trend carbon emissions intensity (CEI) changes is great significance for mitigating climate change issues. The implementation China's Carbon Emissions Trading Scheme (ETS) in 2013 a policy intervention aimed at influencing CEI. impact events makes forecasting complex problem, which poses significant challenges to construction models. We first develop quadratic time-varying nonlinear discrete grey model (QDNDGM(1,1)) assess effect ETS policy. Then, novel effect-based (IE-QDNDGM(1,1)) developed conduct prediction under effect, including an term. Whale Optimization Algorithm (WOA) used calculate parameter. China and find that it can indeed reduce verify IE-QDNDGM(1,1) model's superiority by comparing its predictive performance with three models, one statistical technique, artificial intelligence model. comparative study shows proposed excellent fitting performance. An ablation experiment conducted validate design IE-QDNDGM(1,1). Policy implications are discussed.

Язык: Английский

---

County-level carbon compensation zoning based on China's major function-oriented zones

Journal of Environmental Management, Год журнала: 2024, Номер 367, С. 121988 - 121988

Опубликована: Июль 27, 2024

Язык: Английский

---

Spatio-Temporal Distribution and Spatial Spillover Effects of Net Carbon Emissions: A Case Study of Shaanxi Province, China

Sustainability, Год журнала: 2025, Номер 17(3), С. 1205 - 1205

Опубликована: Фев. 2, 2025

Scientifically evaluating net carbon dioxide (CO2) emissions is the pivotal strategy for mitigating global climate change and fostering sustainable urban development. Shaanxi Province situated in central China, boasts robust energy resources north a significant carbon-sink zone southern Qinling Mountains. Therefore, uncovering spatial distributions of CO2 identifying its influencing factors across cities would furnish crucial theoretical foundation advancing low-carbon development strategies. In this research, from 2005 to 2020 are calculated using carbon-emission-factor calculation model, then Geodetector utilized evaluate single-factor explanatory power two-factor interactions among fourteen various variables, econometric model employed analyze spillover effects these key factors. The results show following: (1) present regional differences ten Province, notably Xi’an City, Yulin Weinan which have recorded remarkable contributions with respective totals reaching 72.2593 million tons, 76.3031 58.1646 tons. (2) Regarding temporal trend changes, aggregate whole province underwent marked expansion 2019. City Shangluo exhibit surges, average annual growth rates soaring at 7.38% 7.39%. (3) From perspective factors, GDP exhibits most pronounced correlation spanning entire province. Meanwhile, foreign investment emerges as contributor specifically City. Moreover, interaction detection reveals factor combinations bi-enhancement, while few intricate non-linear enhancement. (4) SDM regression fixed-effect analysis reveal that city had positive effect on neighboring cities’ emission, scientific research technology services, along per capita construction land, notable negative spillovers, suggesting potential emission reduction benefits cities.

Язык: Английский

---