Integrated assessment and influencing factor analysis of energy-economy-environment system in rural China DOI Creative Commons
Shi Yin, Yuan Yuan

AIMS energy, Год журнала: 2024, Номер 12(6), С. 1173 - 1205

Опубликована: Янв. 1, 2024

<p>With China's economic growth, energy-economy-environment (3E) issues in rural areas have become more prominent. As key for energy consumption and environmental protection, analyzing the influencing factors of 3E system is crucial. We constructed analyzed model using clustering examined relationship between scientific technological talent, level residents, development through principal component regression. The findings showed: (1) Overall improvement development, with coastal non-coastal showing significant differences; (2) room improvement, rapid balanced some regions; (3) talent significantly impacted development; (4) areas, had a greater impact, while both comparable effects. Moreover, 1-unit increase boosted by 0.12 units, increased 0.04 0.06 respectively.</p>

Язык: Английский

Robust operation optimization model for building integrated energy system with demand response mechanism and weighting of consumption responsibility DOI
Lihui Zhang, Qiangnan Cao,

Songrui Li

и другие.

Journal of Renewable and Sustainable Energy, Год журнала: 2025, Номер 17(1)

Опубликована: Янв. 1, 2025

The integrated energy systems (IESs) in buildings locally supply to users and reduce the different types of loss during transmission. IESs are inexpensive highly reliable. However, must simultaneously meet demands multiple load types, consider thermal inertia transmission, user comfort requirements, manage source uncertainties. We established a robust operational optimization model for building considering internal external factors, such as demand response mechanism, comfort, consumption responsibility weighting. also introduced information gap decision theory. simulated analyzed demonstration project IES, drawing following conclusions: (1) operating cost system was 32.66% lower with than without mechanism. (2) For buildings, larger index or equivalent resistance led smaller user-side heating/cooling demand. (3) 6.27% (4) using theory solve operation 10.61% higher that obtained traditional fuzzy chance constraints theory, but more flexible indicative operator risk appetite. This study provides guidance promoting low-carbon operations, green transformation systems, guiding strategies.

Язык: Английский

Процитировано

0

Multi-scenario analysis of green water resource efficiency under carbon emission constraints in the Chengdu-Chongqing urban agglomeration, China: A system dynamics approach DOI
Keyao Yu, Zhigang Li

Ecological Indicators, Год журнала: 2025, Номер 171, С. 113139 - 113139

Опубликована: Янв. 27, 2025

Язык: Английский

Процитировано

0

Internal Excitation of Sustainable Development of Cuban Agricultural Economy from the Perspective of Socialism with Chinese Characteristics in the New Era: Policy and Practice DOI Creative Commons
Lihua Hu,

Chengjiu Wang,

Ping He

и другие.

Sustainable Futures, Год журнала: 2025, Номер unknown, С. 100523 - 100523

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0

Can digitalization promote cities' low-carbon development: Insights from local and neighboring regions DOI Creative Commons

Weijian Du,

Yuhuan Fan, Nini Yuan

и другие.

Energy Strategy Reviews, Год журнала: 2025, Номер 58, С. 101680 - 101680

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0

Integrated assessment and influencing factor analysis of energy-economy-environment system in rural China DOI Creative Commons
Shi Yin, Yuan Yuan

AIMS energy, Год журнала: 2024, Номер 12(6), С. 1173 - 1205

Опубликована: Янв. 1, 2024

<p>With China's economic growth, energy-economy-environment (3E) issues in rural areas have become more prominent. As key for energy consumption and environmental protection, analyzing the influencing factors of 3E system is crucial. We constructed analyzed model using clustering examined relationship between scientific technological talent, level residents, development through principal component regression. The findings showed: (1) Overall improvement development, with coastal non-coastal showing significant differences; (2) room improvement, rapid balanced some regions; (3) talent significantly impacted development; (4) areas, had a greater impact, while both comparable effects. Moreover, 1-unit increase boosted by 0.12 units, increased 0.04 0.06 respectively.</p>

Язык: Английский

Процитировано

0