Carbon emission efficiency measurement and influencing factor analysis of nine provinces in the Yellow River basin: based on SBM-DDF model and Tobit-CCD model

Evaluating complementaries among urban water, energy, infrastructure, and social Sustainable Development Goals in China

Urban areas' performance in water, energy, infrastructure, and socio-economic sectors is intertwined and measurable through Sustainable Development Goals (SDGs) 6-13. Effective synergy among these is critical for sustainability. This study constructs an indicator framework that reflects progress towards these urban SDGs in China. Findings indicate underperformance in SDGs 8-11, suggesting the need for transformative actions. Through network analysis, the research reveals complementarities among these SDGs. Notably, the SDG space divides into socio-economic and ecological clusters, with SDG 6 (Clean Water and Sanitation) central to both. Additionally, SDG 8 (Decent Work and Economic Growth) and SDG 9 (Industry, Innovation, and Infrastructure) act as bridges, while greater synergies exist between SDG 12 (Responsible Consumption and Production) and SDG 13 (Climate Action). An in-depth view at the indicator-level shows a core-periphery structure, emphasizing indicators like SDG 6.2 (Wastewater Treatment Rate) and SDG 6.6 (Recycled Water Production Capacity per capita) as pivotal. This study confirms the urban SDG space's stability and predictiveness, underscoring its value in steering well-aligned policy decisions for sustainable growth.

Synthesis of Fe-N doped porous carbon/silicate composites regulated by minerals in coal gasification fine slag for synergistic electrocatalytic treatment of phenolic wastewater

Coal gasification fine slag (CGFS), as a difficult-to-dispose solid waste in the coal chemical industry, consists of minerals and residual carbon. Due to the aggregate structure of minerals blocking pores and encapsulating active substances, the high-value utilization of CGFS still remains a challenge. Based on the intrinsic characteristics of CGFS, this study synthesized Fe-N doped porous carbon/silicate composites (Fe-NC) by alkali activation and pyrolysis for electrocatalytic degradation of phenolic wastewater. Meanwhile, minerals were utilized to regulate the surface chemical and pore structure, turning their disadvantages into advantages, which caused a sharp increase in m-cresol mineralization. The positive effect of minerals on composite properties was investigated by characterization techniques, electrochemical analyses and density functional theory (DFT) calculations. It was found that the mesoporous structure of the mineral-regulated composites was further developed, with more carbon defects and reactive substances on its surface. Most importantly, silicate mediated iron conversion through strong interaction with H2O2, high work function gradient with electroactive iron, and excellent superoxide radical (•O2-) production capacity. It effectively improved the reversibility and kinetics of the entire electrocatalytic reaction. Within the Fe-NC311 electrocatalytic system, the m-cresol removal rate reached 99.55 ± 1.24%, surpassing most reported Fe-N-doped electrocatalysts. In addition, the adsorption and electrooxidation experiment confirmed that the synergistic effect of Fe-N doped porous carbon and silicate simultaneously promoted the capture of pollutants and the transformation of electroactive molecules, and hence effectively shortened the diffusion path of short-lived radicals, which was further supported by molecular dynamics simulation. Therefore, this research provides new insights into the problem of mineral limitations and opens an innovative approach for CGFS recycling and environmental remediation.

New evidence on the real role of digital economy in influencing public health efficiency

In recent years, the rapid advancement of digital technology has supported the growth of the digital economy. The transformation towards digitization in the public health sector serves as a key indicator of this economic shift. Understanding how the digital economy continuously improves the efficiency of public health services and its various pathways of influence has become increasingly important. It is essential to clarify the impact mechanism of the digital economy on public health services to optimize health expenditures and advance digital economic construction. This study investigates the impact of digital economic development on the efficiency of public health services from a novel perspective, considering social media usage and urban-rural healthcare disparities while constructing a comprehensive index of digital economic development. The findings indicate that the digital economy reduces the efficiency of public health services primarily through two transmission mechanisms: the promotion of social media usage and the widening urban-rural healthcare gap. Moreover, these impacts and transmission pathways exhibit spatial heterogeneity. This study unveils the intrinsic connection and mechanisms of interaction between digital economic development and the efficiency of public health services, providing a theoretical basis and reference for government policy formulation. However, it also prompts further considerations on achieving synergy and interaction between the digital economy and public health services.

Measurement and influencing factors of carbon emission efficiency based on the dual perspectives of water pollution and carbon neutrality

The achievement of the 'Carbon Dioxide Removal' vision has become a crucial strategic objective for national development. However, the carbon emissions produced during wastewater treatment processes hinder the attainment of the 'Carbon Dioxide Removal' targets. Addressing water pollution is not only essential for achieving the goal of 'carbon dioxide removal' but also for enhancing the carbon emission efficiency (CEE). In order to evaluate the CEE of five provinces in the Northwest of a certain developing country in East Asia from 2011 to 2020, this paper proposes a new method that calls the super-efficiency SBM model with unexpected output. Then, the study also analyzes the temporal and spatial evolution characteristics by generating kernel density curves. Furthermore, the Tobit panel regression model is used to examine the factors that influence CEE and analyze the internal mechanisms and reasons behind these factors. Finally, a tailored treatment policy is suggested based on the local water pollution situation. The results show that: (1) The CEE of the entire study area exhibited a consistent upward trend over time. By the conclusion of the study period, the efficiency value had not yet reached 1. (2) Based on the year 2015 as a turning point, the overall gap in CEE of researched areas shows a tendency of first narrowing and then gradually widening. (3) The level of economic development, industrial structure, and green innovative technology are positively correlated with CEE. Conversely, there is an inverse relationship between CEE and the level of urbanization and energy consumption. Through the research conclusion and the reality of water pollution, the policy suggestions to improve the efficiency of urban carbon emission.

Assessing the spatial spillover effects and influencing factors of carbon emission efficiency: a case of three provinces in the middle reaches of the Yangtze River, China

Studying urban carbon emission efficiency is vital for promoting city collaboration in combating climate change. Prior research relied on traditional econometric models, lacking spatial spillover effects understanding at the urban scale. To provide a more comprehensive and visually insightful representation of the evolving characteristics of carbon emission efficiency and its spatial clustering effects and to establish a comprehensive set of indicators to explore the spatial spillover pathways of urban carbon emission efficiency, we conducted an analysis focusing on 42 cities in the middle reaches of the Yangtze River. By employing the index decomposition method, the super-efficiency SBM model, spatial autocorrelation analysis, and the spatial Durbin model, the study calculates the urban carbon emission efficiency from 2011 to 2019 and analyzes the spatial spillover effects and influencing factors of urban carbon emission efficiency. The main conclusions are as follows: (1) Jiangxi Province displayed stable urban carbon emission efficiency evolution, while Hubei and Hunan showed significant internal disparities. (2) Positive spatial correlation exists in urban carbon emission efficiency, with an imbalanced distribution. (3) Various factors influence urban carbon emission efficiency. Technological innovation and economic development have positive direct and indirect impacts, whereas industrial structure, urbanization, population, and energy consumption have negative effects. Spatial spillover effects of vegetation coverage are insignificant. These methods and findings offer insights for future research and policy formulation to promote regional sustainable development and carbon emission reduction.

CCUS technology, digital economy, and carbon emission efficiency: Evidence from China's provincial panel data

Improving carbon emission efficiency is crucial for realizing carbon neutralization. Many critical influencing factors of carbon emission efficiency were identified by previous studies, but they ignored the impact of carbon capture, utilization, and storage (CCUS) technology, which is considered in this study. By employing the panel fixed effect, the moderating effect, and the panel threshold regression models, this study investigates the influence of CCUS technology on carbon emission efficiency and how that impact fluctuates when digital economy is incorporated. Data for China's 30 provinces from 2011 to 2019 is adopted. The results suggest that improving CCUS technology significantly promotes carbon emission efficiency and the promotion effect is positively moderated by digital economy. Considering the level of CCUS technology or digital economy, the effect of CCUS technology on carbon emission efficiency is nonlinear and has significant double-threshold effects. Only when CCUS technology reaches a certain threshold, can it has a significantly positive impact on carbon emission efficiency and that effect has an increasing trend in marginal utility. Meanwhile, with the deepening of digital economy, the relationship between CCUS technology and carbon emission efficiency shows an S-shaped curve trend. Those findings, first combining CCUS technology, digital economy, and carbon emission efficiency together, reflect the significance of advancing CCUS technology and adjusting the development of digital economy for achieving sustainable low-carbon development.

Study on the influence of industrial intelligence on carbon emission efficiency-empirical analysis of China's Yangtze River Economic Belt

How to achieve the goal of "carbon peak and carbon neutrality" and explore the compatibility of industrial and ecological civilization is a major challenge for China today. This study analyzes the impact of industrial intelligence on industrial carbon emissions efficiency in 11 provinces of China's Yangtze River Economic Belt, measuring the efficiency of industrial carbon emissions through the non-expected output slacks-based measure (SBM) model, selecting industrial robot penetration to measure the level of industrial intelligence, establishing a two-way fixed model to verify the impact of industrial intelligence on carbon emission efficiency, and testing for intermediary effects and regional heterogeneity. The results show that: (1) the industrial carbon emission efficiency of the 11 provinces shows year-over-year improvement, with significant differences between upstream, midstream, and downstream, where downstream is the highest and upstream is the lowest. (2) The development of industrial intelligence is highly uneven, with the upstream level being the weakest. (3) Industrial intelligence can improve the efficiency of industrial carbon emissions by enhancing green technological innovation and energy use efficiency. (4) The effect of industrial intelligence on industrial carbon emission efficiency also shows regional heterogeneity. Finally, we present policy recommendations. This research provides mathematical and scientific support for achieving carbon reduction targets at an early stage and helps accelerate the construction of a modern, low-carbon China.

How does technological innovation affect carbon emission efficiency in the Yellow River Economic Belt: the moderating role of government support and marketization

The Yellow River Economic Belt (YREB) is a fundamental ecological protection barrier for China. Its carbon pollution issues are currently severe owing to the extensive energy consumption and unsatisfactory industrial constructions. In this context, this paper estimates carbon emission efficiency (CEE) based on the panel data from 56 cities in the YREB during the period 2006-2019 and analyzes its spatial distribution characteristics. Additionally, the spatial Durbin model (SDM) is utilized to examine the effect of technological innovation (TI) on CEE as a result of the moderating effects of government support (GS) and marketization (MA), respectively. The results indicated that (i) in the YREB, CEE exhibited significant spatial autocorrelation characteristics; (ii) TI negatively affected local CEE; (iii) the moderating effect of local GS on the relationship between TI and CEE in the local area was negative, but its spatial spillover effect was still not significant; (iv) the moderating effect of local MA on the relationship between TI and CEE in the local area was also negative, but positive in the surrounding areas. Based on the empirical analysis, a series of policy suggestions are proposed to improve the YREB's CEE.

Spatiotemporal Dynamic Distribution, Regional Differences and Spatial Convergence Mechanisms of Carbon Emission Intensity: Evidence from the Urban Agglomerations in the Yellow River Basin

Low-carbon transition is of great importance in promoting the high-quality and sustainable development of urban agglomerations in the Yellow River Basin (YRB). In this study, the spatial Markov chain and Dagum's Gini coefficient are used to describe the distribution dynamics and regional differences in the carbon emission intensity (CEI) of urban agglomerations in the YRB from 2007 to 2017. Additionally, based on the spatial convergence model, this paper analyzed the impact of technological innovation, industrial structure optimization and upgrading, and the government's attention to green development on the CEI's convergence speed for different urban agglomerations. The research results show that: (1) The probability of adjacent type transfer, cross-stage transfer, and cross-space transfer of the CEI of urban agglomerations in the YRB is small, indicating that the overall spatiotemporal distribution type of CEI is relatively stable. (2) The CEI of urban agglomerations in the YRB has decreased significantly, but the spatial differences are still significant, with a trend of continuous increase, and regional differences mainly come from the differences between urban agglomerations. (3) Expanding innovation output, promoting the optimization and upgrading of industrial structure, and enhancing the government's attention to green development has a significant positive effect on the convergence rate of the CEI of urban agglomerations in the YRB. This paper holds that implementing differentiated emission reduction measures and actively expanding regional collaborative mechanisms will play an important role in reducing the spatial differences in carbon emissions in urban agglomerations in the YRB, realizing the goals of peak carbon and carbon neutrality.

Spatial Difference and Convergence of Ecological Common Prosperity: Evidence from the Yellow River Basin in China

Analyzing the spatial difference and convergence of ecological common prosperity (ECP) in the Yellow River Basin (YRB) will be beneficial for the environmental governance and multi-regional economic coordination. Based on the panel data of 97 cities in the YRB from 2003 to 2019, this paper measured and analyzed the index of ECP, the Gini coefficient of ECP, and the convergence of ECP. The results indicate that the ECP of YRB shows a steady growth trend (with an average growth rate of 4.71% yearly) and the overall differences are low (average Gini coefficient is 0.1509 from 2003 to 2019). In different areas, the Gini coefficient between the medium-stream and downstream of YRB is the largest (average value of Gini coefficient is 0.1561). From the decomposition of the overall differences of ECP, the contribution degree of the density of transvariation is the highest for annual average, with a contribution rate of 43.37%, the rate of intra-regional and the inter-regional differences are 31.86% and 24.77%, respectively. The results indicate that the overall differences of ECP in YRB are getting smaller because of cooperation and governance, but the differences between and within regions exist because of geographical feature. There is a significant spatial β convergence trend of ECP, the convergence rate in the upstream and downstream area is faster under the economic geographical matrix than others, and the rate in the medium-stream area is faster under the administrative adjacency matrix. Therefore, strengthening economic and environmental cooperation between and within regions is more beneficial to achieve a better quality of life, as well as the long-term goals of 2035.

The evolutionary characteristics and influencing factors of total carbon productivity: evidence from China

In order to systematically understand the evolution of total factor carbon productivity and explore its influence mechanism, based on panel data of 30 Chinese provinces from 2005 to 2019, the slacks-based measure of directional distance functions model and the Luenberger index are used to estimate the evolution of total factor carbon productivity, and the SYS-GMM model is constructed to explore the drivers of total factor carbon productivity and its influence effect. The results show that from 2005 to 2019, the overall level of total factor carbon productivity was low, but its growth index and decomposition term both showed an increasing trend; the development of total factor carbon productivity has regional differences. Only the eastern, northern, and middle Yellow River economic regions experience positive growth in total factor carbon production. The downward trend of total factor carbon productivity is most significant in the northwest and southwest economic regions, with - 2.577% and - 1.463%, respectively; improvements in scale technology are the main reasons for improving total factor carbon productivity across time and regions; economic growth and environmental regulations contribute to total factor carbon productivity at 1% significance level, and industrial structure has a negative impact. Foreign direct investment inhibits total factor carbon productivity, but the effect is not significant. Based on these findings, this paper provides an effective reference for achieving the goal of low-carbon sustainable development and improving total factor carbon productivity.

Differentiation Analysis on Carbon Emission Efficiency and Its Factors at Different Industrialization Stages: Evidence from Mainland China

Industrial production is currently the main source of global carbon emissions. There are obvious differences in regional carbon emission efficiencies (CEE) at different industrial stages. We investigate CEE and explore its factors in mainland China at different industrialization stages from 2008-2020 using the super-SBM model with an undesirable output and the STIRPAT model. There is significant spatial heterogeneity in regional CEE, with gaps gradually widening. CEE's spatial heterogeneity in mid-industrialized provinces is narrowing, while in late-industrialized and post-industrialized provinces, it is widening. CEE's factors also differ in provinces at different industrialization stages. At the mid-industrialization stage, the industrial structure (IS) is the dominant factor, while population urbanization (PU) is dominant at the late-industrialization stage, and both PU and IS are dominant at the post-industrialization stage. Based on CEE's characteristics at different industrialization stages, we propose suggestions for green development.

Research on carbon emission measurement and low-carbon path of regional industry

As industry is the world's leading carbon emitter, promoting industrial carbon reduction is of key significance to carbon peak and carbon neutrality. Using a data-driven method, based on the collection and processing of relevant data from statistical yearbooks and others, we analyze the efficiency and amount of carbon emission of each industrial sector after processing multi-dimensional data by the improved IPCC EF method of calculating carbon emissions. In addition, we adopt the LMDI decomposition method for data modeling to measure the contribution of energy efficiency, industrial structure, GDP per capita, and population size to carbon emission changes, to identify targets for industrial carbon reduction, and to propose a targeted optimization path for carbon emission. We show how the method is implemented by taking the statistics of Anhui Province from 2010 to 2019 as an example and advises on an optimization path for carbon emission in Anhui Province. This study is of both theoretical and practical significance as it provides theoretical and methodological support for the low-carbon development of the regional industry, and provides a reference for other countries and regions to explore the path of low-carbon and environment-friendly green transformation and upgrading.

Spatiotemporal Evolution and Influencing Factors of Carbon Emission Efficiency in the Yellow River Basin of China: Comparative Analysis of Resource and Non-Resource-Based Cities

Comparing the carbon emission efficiency (CEE) of resource and non-resource-based cities in the Yellow River Basin (YRB) can guide their synergistic development and low-carbon transition. This study used the super-efficiency slacks-based measure (super-SBM) model to measure the CEE of cities in the YRB. Kernel density estimation and Theil index decomposition methods were used to explore the spatiotemporal evolutionary patterns, and a panel regression model was established to analyze the influencing factors of CEE. The research results showed that the CEE of the two types of cities have an overall upward trend in time, with a widening regional gap. Resource-based cities mainly displayed the characteristics of decentralized regional agglomeration, while non-resource-based cities mainly showed the characteristics of convergent regional agglomeration. Panel regression results showed that the levels of economic development, indus-trial structure, and population density are significantly positively correlated with CEE in the YRB, while foreign direct investment and resource endowment are significantly negatively correlated with CEE. Except for economic development and industrial structure, there is some variability in the contribution of the remaining influencing factors to the CEE of the resource and non-resource-based cities. The research results suggest developing classification measures for low-carbon transition in the YRB.

Regional Differences and Convergence of Carbon Emissions Intensity in Cities along the Yellow River Basin in China

Since the ecological protection and high-quality development of the Yellow River Basin (YRB) in China have become a primary national strategy, the low-carbon economy is crucial. To formulate effective emission mitigation policies for the YRB, we need to comprehensively understand the characteristics of the spatial agglomeration of the carbon emissions intensity in the YRB and its regional heterogeneity. Therefore, based on the relevant data from 2005 to 2017, we first scientifically measure the carbon emissions intensity of 57 cities along the YRB. Then, we analyze the spatial agglomeration characteristics and long-term transfer trends of carbon emission intensity using exploratory spatial data analysis methods and Markov chains. Finally, the Dagum Gini coefficient and the variation coefficient method are used to study the regional differences and differential evolution convergence of the carbon emissions intensity in the YRB. The results show that the carbon emissions intensity of the YRB has dropped significantly with the spatial distribution characteristics “high in the west and low in the east”, and there is a significant spatial autocorrelation phenomenon. In addition, the probability of a shift in urban carbon intensity is low, leading to a “club convergence” and a “Matthew effect” in general and across regions. Inter-regional differences have always been the primary source of spatial differences in carbon emissions intensity in the YRB, and the intra-regional differences in carbon emissions intensity in the lower YRB show a significant convergence phenomenon. The research results may provide a reference for the regional coordinated development of a low-carbon economy in the YRB, and serve to guide the win-win development model of ecological environment protection and economic growth in the YRB.

Carbon Emission Prediction Model and Analysis in the Yellow River Basin Based on a Machine Learning Method

Excessive carbon emissions seriously threaten the sustainable development of society and the environment and have attracted the attention of the international community. The Yellow River Basin is an important ecological barrier and economic development zone in China. Studying the influencing factors of carbon emissions in the Yellow River Basin is of great significance to help China achieve carbon peaking. In this study, quadratic assignment procedure regression analysis was used to analyze the factors influencing carbon emissions in the Yellow River Basin from the perspective of regional differences. Accurate carbon emission prediction models can guide the formulation of emission reduction policies. We propose a machine learning prediction model, namely, the long short-term memory network optimized by the sparrow search algorithm, and apply it to carbon emission prediction in the Yellow River Basin. The results show an increasing trend in carbon emissions in the Yellow River Basin, with significant inter-provincial differences. The carbon emission intensity of the Yellow River Basin decreased from 5.187 t/10,000 RMB in 2000 to 1.672 t/10,000 RMB in 2019, showing a gradually decreasing trend. The carbon emissions of Qinghai are less than one-tenth of those in Shandong, the highest carbon emitter. The main factor contributing to carbon emissions in the Yellow River Basin from 2000 to 2010 was GDP per capita; after 2010, the main factor was population. Compared to the single long short-term memory network, the mean absolute percentage error of the proposed model is reduced by 44.38%.

Effects of Industrial Structure Adjustment on Pollutants Discharged to the Aquatic Environment in Northwest China

Northwest China is located along China’s Belt and Road Initiative routes and represents the frontier and core region for China’s construction and development of the Silk Road Economic Belt. In recent years, the conflict between economic development and environmental pollution has become increasingly intense in this region, with the latter mainly caused by disorderly industrialization brought about by rapid urbanization processes. Inappropriate industrial structure is the primary reason for environmental degradation in Northwest China, which has limited precipitation and available water. Due to its fragile aquatic environment and unsustainable use of water resources, the pollution and degradation of the aquatic environment has become a bottleneck that severely restricts the sustainable development of China’s northwest region. In the present study, five provinces or autonomous regions in Northwest China were selected as the study objects. Based on the vector autoregressive (VAR) model, quantitative research methods, such as impulse response function and variance decomposition analysis, were applied to quantify the dynamics between industrial structure adjustment and changes in industrial pollutant discharges to the aquatic environment, so that the impact of industrial structure adjustment on pollutants discharged to the aquatic environment could be quantified and characterized. Therefore, the present study has both theoretical and practical significance. The conclusions are as follows: (1) In general, industrial structure in most provinces in Northwest China imposes a positive effect over the discharge of pollutants to the aquatic environment. Adjusting industrial structure and reducing the proportion of secondary industry present can to some extent promote reductions in the discharge of pollutants to the aquatic environment. However, such beneficial effects may vary among different provinces. (2) Specifically, for Gansu, province industrial structure adjustment could help reduce the discharge of pollutants to the aquatic environment effectively during the early stages, but this positive effect gradually weakens and disappears during the later stages. In Qinghai province, industrial structure adjustment could not help reduce the discharge of pollutants to the aquatic environment effectively during the early stages, but a positive effect gradually increases and continues to function later. The performance in Shaanxi and Xinjiang provinces was quite similar, with industrial structure adjustment helping to effectively reduce the discharge of pollutants to the aquatic environment over a long period of time. This positive effect can play a more sustained and stable role. For Ningxia province, industrial structure adjustment can not only help significantly reduce the discharge of pollutants to the aquatic environment but also displays a significant positive effect. (3) Given the specific conditions and characteristics of the region under study, relevant policies for industrial structure adjustment should be formulated and implemented.