Decomposition and decoupling analysis of carbon emissions in the Yellow River Basin: evidence from urban agglomerations.

A comprehensive understanding of carbon emission reduction and decoupling in urban agglomerations of the Yellow River Basin (YRB) has significant theoretical and practical value for formulating precise carbon reduction policies and achieving ecological conservation and high-quality development in the region. This study utilized a generalized Divisia index decomposition model to identify the primary driving factors behind carbon emission changes in urban agglomerations of the YRB. Based on this, a model measuring decoupling efforts was constructed to systematically investigate the decoupling relationship between carbon emissions. The research findings indicate that technological progress and output scale are two primary drivers of carbon emission increases in the YRB and its urban agglomerations, whereas technological carbon intensity, output carbon intensity, and energy carbon intensity play key roles in reducing carbon emissions. Except for a few years, the YRB and Jiziwan metropolitan area (JWMA) did not exhibit decoupling effects on carbon emissions. The Shandong Peninsula Urban Agglomeration (SPUA) and Central Plains Urban Agglomeration (CPUA) showed strong decoupling effects from 2016 to 2019. The Guanzhong Plain Urban Agglomeration (GPUA) demonstrated a strong decoupling effect from 2013 to 2019 (except from 2016 to 2017). The Lanxi Urban Agglomeration (LXUA) exhibited a strong decoupling effect from 2014 to 2019. Technological carbon intensity plays a decisive role in the transition from non-decoupling to decoupling. Therefore, the government must increase investments in green and low-carbon technologies and strictly implement carbon reduction measures. Thus, the YRB and its urban agglomerations have considerable potential for carbon emission reduction and strong decoupling effects.

Dynamic evolution characteristics and driving factors of carbon emissions in prefecture-level cities in the Yellow River Basin of China.

This paper focuses on the spatiotemporal evolution characteristics, as well as the driving factors, of carbon emissions in the prefecture-level cities in the Yellow River Basin (YB). The paper's findings will aid in promoting ecological conservation and high-quality development in the region. The initiatives undertaken in the YB are a significant national strategy towards achieving carbon peaking and carbon neutrality. To fully investigate the spatiotemporal evolution process, as well as the typical characteristics of their carbon emissions, conventional, and spatial Markov transition probability matrices were developed utilizing YB's panel data for 55 prefecture-level cities from 2003 to 2019. The generalized Divisia index decomposition method (GDIM) cleverly uses this data to conduct a complete analysis of the dynamics and driving processes influencing the change in carbon emissions in these cities. However, the evolution of carbon emissions in prefecture-level cities has reached a point of stability that maintains the original state, making it challenging to make meaningful short-term progress. The data indicates that prefecture-level cities in the YB are emitting more carbon dioxide on average. Neighborhood types in these cities significantly influence the transformation of carbon emissions. Low-emission areas can encourage a reduction in carbon emissions, whereas high-emission areas can encourage an increase. The spatial organisation of carbon emissions exhibits a "high-high convergence, low-low convergence, high-pulling low, low-inhibiting high" club convergence phenomenon. Carbon emissions rise with per capita carbon emissions, energy consumed, technology, and output scale, whereas it falls with carbon technology intensity and output carbon intensity. Hence, instead of enhancing the role of increase-oriented variables, prefecture-level cities in the YB should actively engage these reduction-oriented forces. The YB's key pathways for lowering carbon emissions include boosting research and development, promoting and applying carbon emission reduction technologies, lowering output carbon intensity and energy intensity, and improving energy use effectiveness.

Regional Disparities in Obesity Among a Heterogeneous Population of Chinese Children and Adolescents.

Importance: Obesity is a public health challenge in China, but the geographical profiles of overweight and obesity among Chinese children are limited. Objective: To examine regional disparities in the prevalence of obesity among the heterogeneous population of Chinese children and adolescents to provide a more accurate profile of obesity among children in China. Design, Setting, and Participants: The Prevalence and Risk Factors for Obesity and Diabetes in Youth (PRODY) study was a cross-sectional survey study conducted from January 1, 2017, to December 31, 2019, among 201 098 children aged 3 to 18 years from 11 provinces, autonomous regions, and municipalities that produced a sample of Chinese children with a full range of ages and wide geographical coverage using a multistage, stratified, cluster-sampling design. Exposures: Five regions geographically representative of China (northern, eastern, southern, western, and central). Main Outcomes and Measures: The body weights and heights of all participants were measured. Multilevel, multinomial logistic regression models were used to estimate the prevalence of overweight and obesity. Results: Among 201 098 healthy children (105 875 boys [52.6%]; mean [SD] age, 9.8 [3.8] years) from eastern, southern, northern, central, and western China, the highest obesity prevalence was estimated for children aged 8 to 13 years in northern China (from 18.8% [95% CI, 16.2%-21.7%] to 23.6% [95% CI, 20.5%-26.9%]) and for boys aged 3 to 6 years in western China (from 18.1% [95% CI, 10.4%-29.4%] to 28.6% [95% CI, 14.3%-49.0%]). Boys had a higher prevalence than girls of obesity only in eastern and northern China, with a mean difference in prevalence of 4.6% (95% CI, 3.8%-5.4%) and 7.6% (95% CI, 6.5%-8.6%), respectively. Conclusions and Relevance: In this survey study, substantial geographic disparities in the prevalence of obesity and overweight were found among the heterogeneous population of Chinese children. The results suggest that special attention should be paid to vulnerable children and that regionally adapted interventions are needed to efficiently mitigate obesity in children.

The empirical decomposition and peak path of China's tourism carbon emissions.

Carbon emissions from tourism are an important indicator to measure the impact of tourism on environmental quality. As the world's largest industry, tourism has many related industries and is a strong driver of energy consumption. The emission reductions it can achieve will directly determine whether China's overall carbon emission reduction target can be met. This paper analyzes the drivers of the evolution of carbon emissions from the tourism industry in China over the period 2000-2017 as a research sample using the Generalized Dividing Index Method (GDIM), and on this basis, it uses scenario analysis and Monte Carlo simulation to predict the carbon peak in tourism for the first time. The research results show that the scale of industry and energy consumption are the key factors leading to increased tourism carbon emissions, and the carbon intensity of tourism industry, energy consumption carbon intensity, investment efficiency, and energy intensity are the main factors leading to reduced carbon emissions from tourism. The scale of investment and the carbon intensity of investment have a dual effect; the scenario analysis and Monte Carlo simulation used to predict peak carbon in China's tourism industry show that the peak carbon will occur approximately in 2030. The government needs to further guide and encourage the tourism industry to increase investment activities targeting energy conservation and emission reduction. Under the conditions of strictly implementing energy conservation and emission reduction measures and vigorous promotion of the transformation and upgrading of tourism development methods, the tourism industry will have considerable potential to reduce carbon emissions.

Carbon emissions, the industrial structure and economic growth: Evidence from heterogeneous industries in China.

A comprehensive understanding of the relationships among carbon emissions, the industrial structure and economic growth holds great significance for China's transition to a low-carbon economy, industrial structure optimization, and achievement of energy conservation and emission reduction targets. We selected six major industrial sectors (agriculture, industry, construction, transportation, retail and accommodation and other industries) as research objects, introduced the extended STIRPAT decomposition model, Tapio decoupling model and the grey relation analysis to discuss the relationship among the three. Results showed that (i) since 2000, the proportions of value added of agriculture, manufacturing, and transportation are negatively correlated with carbon emissions, while those of construction, retail and accommodation, and other industries are positively correlated with carbon emissions. (ii) The overall economic growth and carbon emissions of these six major industries have experienced the process of decoupling-coupling-decoupling-coupling-decoupling. (iii) The relevance of these six industries to GDP is ranked as follows: transportation > manufacturing > retail andaccommodation > agriculture > construction > other industries. Additionally, accelerating the achievement of a clean energy structure, strengthening the strength and speed of industrial structure adjustment and reducing the dependence on fossil energy are the key steps for China to reach carbon emissions peak goal.

Ecological efficiency in China and its influencing factors-a super-efficient SBM metafrontier-Malmquist-Tobit model study.

Ecological problem is one of the core issues that restrain China's economic development at present, and it is urgently needed to be solved properly and effectively. Based on panel data from 30 regions, this paper uses a super efficiency slack-based measure (SBM) model that introduces the undesirable output to calculate the ecological efficiency, and then uses traditional and metafrontier-Malmquist index method to study regional change trends and technology gap ratios (TGRs). Finally, the Tobit regression and principal component analysis methods are used to analysis the main factors affecting eco-efficiency and impact degree. The results show that about 60% of China's provinces have effective eco-efficiency, and the overall ecological efficiency of China is at the superior middling level, but there is a serious imbalance among different provinces and regions. Ecological efficiency has an obvious spatial cluster effect. There are differences among regional TGR values. Most regions show a downward trend and the phenomenon of focusing on economic development at the expense of ecological protection still exists. Expansion of opening to the outside, increases in R&D spending, and improvement of population urbanization rate have positive effects on eco-efficiency. Blind economic expansion, increases of industrial structure, and proportion of energy consumption have negative effects on eco-efficiency.