Multi-objective optimisation model of a low-cost path to peaking carbon dioxide emissions and carbon neutrality in China.

A low-cost path system for achieving carbon neutrality in China was modelled using multi-objective programming by integrating industrial production, electric power, heating, transportation, and forest carbon sequestration. We aimed to minimise the total system cost, CO2 emissions, and air pollutants. The constraints included China's targets of peaking CO2 emissions before 2030; achieving carbon neutrality before 2060; ensuring industry, power, heating, and transportation supplies; promoting green energy; and implementing emission control. The model accounted for industries with high coal consumption, such as steel and chemical industries. Various power sources were considered, including coal-fired, nuclear, wind, and solar energy. Forest carbon sink and carbon capture and storage technologies were employed to achieve the emission reduction goals. The model, which was validated using available research data, offered cost-effective path schemes and exhibited high validity. Our findings emphasise the importance of structural adjustments and emission control, with electric power, heating, and transportation sectors showing higher feasibility and providing greater contributions to achieving carbon neutrality than other industries. Conversely, industrial transformation in sectors such as iron and steel, chemical, and construction materials had low feasibility and limited contribution. The modelling outcomes provide valuable insights for developing low-cost, carbon emission-targeted transportation structures in China's complex system. The results presented here demonstrate the global applicability of this method in contributing to plans aimed at meeting key carbon reduction targets.

[Synergistic Paths of Reduced Pollution and Carbon Emissions Based on Different Power Demands in China].

Nowadays, China is faced with two strategic tasks:improving ecological environmental quality and realizing carbon neutrality and carbon peaking. Synergy to reduce pollution and carbon emissions has become an inevitable choice for the comprehensive green transition of economic and social development in China. The electric power sector will play an important role in the transition process. Based on different power demand scenarios, a multi-objective model was constructed to achieve carbon peaking and carbon neutrality at a low cost, and the optimal path scheme of carbon emission reduction synergy was obtained. The results showed that under the premise of achieving carbon peaking and carbon neutrality as scheduled, pollution reduction and carbon reduction had good synergies, and their synergistic control could effectively facilitate the realization of the low-carbon transition. Optimizing the power generation structure of the electric power sector was the key measure to achieving the synergistic effect of pollution reduction and carbon reduction. During the study period, the proportion of thermal power decreased continuously, and the proportion of clean power exceeded 92.5%. The emissions of carbon dioxide and major air pollutants were significantly different under different power demands. Carbon dioxide emissions were most affected by power demand. The peak carbon dioxide emissions under low power demand, medium power demand, and high power demand were 9.416 billion, 10.409 billion, and 10.746 billion t, respectively. The emissions of sulfur dioxide, nitrogen oxide, and particulate matter also showed an increasing trend in the low power demand, medium power demand, and high power demand scenarios. The increase in power demand only increased the pressure of power generation structure adjustment within the electric power sector, without affecting the output and activity level of other sectors, that is, the pressure of emission reduction in the electric power sector caused by power demand did not show the trend of transmission between sectors.

A comparative study of stage characteristics and factorial decomposition of CO2 emissions between China and the USA.

CO2 contributes a lot to the greenhouse effect. The total CO2 emissions of the two countries, China and the USA, as the world's top two economies, have exceeded 40% of the total global carbon emissions. In this context, the exploration of the evolution of carbon emissions from energy consumption in China and the USA and the comparison of the characteristics of carbon emission drivers in different periods play a significant role in the policy formulation and climate change cooperation between China and the USA. In this study, the BP structural breakpoint test was used to divide the carbon emission stages of China and the USA from 1970 to 2019. The generalized Divisia index model (GDIM) was developed to decompose the growth of carbon emissions in China and the USA into eight items, GDP, carbon intensity of GDP, energy use, carbon intensity of energy, population, carbon emissions per capita, GDP per capita, and energy intensity, and to analyze the characteristics and cumulative contribution of carbon emission drivers at each stage. Based on the stage and cumulative characteristics of carbon emissions between China and the USA, the USA should take the initiative to assume the legal responsibility of carbon emissions and further deepen the cooperation with other countries in the field of climate change. China should transform the economic growth mode, optimize the energy structure, and improve the efficiency of resource utilization to help achieve the peaking carbon emissions and the carbon neutrality smoothly.

Research on Urban Spatial Connection and Network Structure of Urban Agglomeration in Yangtze River Delta-Based on the Perspective of Information Flow.

Exploration of urban spatial connections and network structures of urban agglomeration in the Yangtze River Delta, as well as its influencing factors, is of great significance regarding optimization of the development pattern of the Yangtze River Delta urban agglomeration and promotion of regional high-quality development. Therefore, based on Baidu index data in 2015 and 2019, this paper first analyzes the spatiotemporal variation characteristics of information-flow connections in the Yangtze River Delta urban agglomeration. Then it uses social network analysis to explore the information-flow network structure in the Yangtze River Delta urban agglomeration, and finally explores the influencing factors of information-flow intensity in the Yangtze River Delta urban agglomeration. The main conclusions are as follows: (1) The total amount of information flow in the Yangtze River Delta urban agglomeration has had no obvious change, and the coverage of information flow in the central urban circle has expanded. (2) The network hierarchy presents a relatively stable "pyramid" distribution pattern, which tends to develop into a "spindle" pattern. (3) The overall network density of the Yangtze River Delta urban agglomeration is high and is increasing. The backbone network is a "triangle" structure. The central cities in the region are stable, and the subgroups are adjacent to each other geographically. (4) Gross Domestic Product, resident population of the region and the number of Internet broadband subscribers all have important effects on the total information flow, among which the number of Internet broadband subscribers has the greatest effect on the total information flow. In addition, urban functions and their positioning, urban events, history and culture, and other factors that are difficult to quantify also have a certain impact on the information-flow network among cities.