Impact Factors and Structural Pathways of Carbon Emissions in the Power Sector of the Beijing–Tianjin–Hebei Region Using MRIO Analysis

Atmosphere, Journal Year: 2025, Volume and Issue: 16(2), P. 177 - 177

Published: Feb. 5, 2025

The accelerated growth of the global economy has given rise to a multitude environmental concerns that demand immediate attention. At this juncture, total carbon emissions are exhibiting gradual increase. China, United States, India, Russia, and Japan represent top five countries in terms emissions, collectively accounting for approximately 60% total. Of these, China’s highest world, representing over 30% As urbanization accelerates, from urban agglomerations constitute substantial share nation’s rendering clusters critical issue. In context agglomerations, Beijing–Tianjin–Hebei region, due factors such as industrial structure, accounts relatively high proportion 11% national future trajectory region will significantly impact high-quality development entire cluster. Consequently, research on is vital importance. This paper takes power industry subject, analyzes its status, builds multi-regional input–output model based tables data each province. study explores key influencing 2012 2017 transfer structural evolution perspective clarify reduction responsibilities provide references recommendations formulation regional collaborative emission policies. results show direct account higher compared indirect it generates by driving other industries. Industries with path include coal mining selection, equipment manufacturing, transportation, services, etc. capital input process Tianjin Hebei Beijing accompanied transfer. Promoting widespread adoption technologies have an effective suppressive effect especially Hebei; should pay attention stimulating increased final emissions; between regions industries shows downward trend sector undergoes transformation.

Language: Английский

A Study on the Decoupling Effect Between Economic Development Level and Carbon Dioxide Emissions: An Empirical Analysis Based on Mineral Resource-Based Cities in Southwest China

Sustainability, Journal Year: 2024, Volume and Issue: 16(22), P. 10081 - 10081

Published: Nov. 19, 2024

Mineral resource-based cities (MRBCs) refer to with mining and processing of mineral resources as the main industry, so there is a close relationship between their economic development resource consumption. However, this often hinders its rapid transition towards diversification low-carbon models. Based on quantifying index level 18 MRBCs in southwest China, paper has employed Tapio elasticity coefficient method (Tapio model) Environmental Kuznets Curve (EKC curve) analyze decoupling effect carbon dioxide. After deep research “decoupling” phenomenon dynamic changes emissions, aimed explore transformation path suitable for each city. The results have indicated that: (1) overall trend dioxide emissions increasing, but growth rate gradually slowing down, effectively controlling situation emissions. (2) shows an upward trend, increases, which signifies positive development. (3) began China 2013, was achieved 2019.

Language: Английский

Carbon-Efficient Scheduling in Fresh Food Supply Chains with a Time-Window-Constrained Deep Reinforcement Learning Model

Sensors, Journal Year: 2024, Volume and Issue: 24(23), P. 7461 - 7461

Published: Nov. 22, 2024

Intelligent Transportation Systems (ITSs) leverage Internet of Things (IoT) technology to facilitate smart interconnectivity among vehicles, infrastructure, and users, thereby optimizing traffic flow. This paper constructs an optimization model for the fresh food supply chain distribution route products, considering factors such as carbon emissions, time windows, cooling costs. By calculating emission costs through taxes, aims minimize With a graph attention network structure adopted describe node locations, accessible paths, data with collection windows path planning, it integrates solve optimal routes, taking into account emissions under varying temperatures. Extensive simulation experiments comparative analyses demonstrate that proposed time-window-constrained reinforcement learning provides effective decision-making information product transportation distribution, controlling logistics costs, reducing emissions.

Language: Английский