Evaluating the Coordination of Industrial-Economic Development Based on Anthropogenic Carbon Emissions in Henan Province, China

IntroductionPopulation expansion and economic development increased global greenhouse gas emissions, leading to serious environmental degradation. China, the world's largest developing country and promoter of the “Belt and Road Initiative” (BRI), accounts for 28.8% of the world"s total energy carbon emissions. How to reduce energy consumption to achieve the “double carbon” target (i.e., carbon peaking and carbon neutrality) and promote the implementation of Green BRI is still a serious challenge that China needs to face. MethodsWe evaluated China's carbon emissions using three indicators (i.e., total carbon emission, carbon intensity, and carbon emissions effect), and used spatial analysis to reveal the spatial and temporal trends of China's carbon emissions. In addition, the LMDI model was adopted to explore the driving mechanism of carbon emissions, so as to seek a path that can achieve harmonious economic and environmental development, as well as the “double carbon” target.ResultsChina's total carbon emission increased at a rate of 226.12% from 2000 to 2019, while the carbon intensity decreased at a rate of 48.84%. Carbon emission showed a trend of increasing and then decreasing from southwest to northeast. From 2000 to 2019, the total carbon emission, Gross Domestic Product (GDP), population size and total energy consumption are growing in synergy. Economic and population effects are positively related to carbon emissions, while technology effects are negatively related to it, indicating technological innovations contribute to the reduction of carbon emissions.DiscussionSome suggestions were proposed to control carbon emissions with a view to helping policy makers to formulate relevant policies. The findings provide a scientific basis and reference for the country to achieve the “double carbon” target and the low-carbon sustainable development of BRI.

There are disagreements regarding the accuracy of estimation and spatial distribution of carbon emissions in China. It is of great significance to estimate a more detailed carbon emission inventory for China and analyze the carbon emission characteristics of different regions. This study comprehensively estimated carbon dioxide and methane emissions (and their spatial distributions) across eight carbon-emitting sectors in 360 prefecture-level cities in China in 2020. The results indicated that total carbon emissions in China amounted to 146.00 × 108 t, with carbon dioxide and methane accounting for 95.87% and 4.13%, respectively. The industrial sector was the main source of carbon emissions, accounting for 75.42% of the total. The North China Plain, the Northeast Plain, and the Sichuan Basin were identified as the carbon emission hotspot areas with the most intensive carbon emission densities. Among the clustered four carbon emission zones based on carbon emission density and economic carbon intensity, the High Carbon Emission Density and High Economic Carbon Intensity zones accounted for 41.73% of total carbon emissions. To achieve carbon neutrality, it is essential to devise emission reduction strategies for specific areas by thoroughly considering spatially explicit variation at the prefecture level, with a focus on primary carbon-emitting cities and sectors.

Effectively exploring the impacts of urban spatial structures on carbon dioxide emissions is important for achieving low-carbon goals. However, most previous studies have examined the impact of urban spatial structure on total carbon emissions based only on polycentricity. Fine-grained studies on subsectoral carbon emissions and other dimensions of urban spatial structure are lacking. Therefore, our study comprehensively explores the impact of urban dispersion and polycentricity on total carbon emissions and carbon emissions of four subsectors (industry, power, civilian, and transportation) from 2012 to 2017 while considering the effects of city size. Results reveal that the nighttime light data is useful for measuring urban spatial structure, and a polycentric, decentralized urban spatial structure correlates with the reduced total carbon emissions and transportation carbon emissions. Meanwhile, a decentralized urban spatial structure gives rise to lower industrial carbon emissions and civilian carbon emissions, whereas a multicenter urban spatial structure contributes to minimizing carbon emissions from power systems. However, in small and medium-sized cities, urban spatial structure differently affects the total carbon and transportation carbon emissions.

With the economic and social development and the improvement of peoples living standards, the urban carbon emission increases. Studying the influence of industrial structure and spatial patterns on carbon emission under the economic development of different cities can help to find the main factors affecting carbon emission, formulate policies to solve the problem according to local conditions, and provide an important scientific basis for the transformation of economic growth mode and the construction of low-carbon cities. This paper draws conclusions by analyzing the trend curves of urban carbon emissions and economic factors over the years. Different regions of the citys economic development potential differences are obvious, and the potential for greater spatial concentration of cities; citys economic development potential in the spatial and temporal distribution of key cities and major urban agglomerations as the basis for the point - line - surface in order to promote. economically developed cities, the total carbon emissions, carbon emissions growth rate of a declining trend; economically underdeveloped areas, the total carbon emissions, carbon emissions growth rate of a year-on-year trend; the degree of carbon emissions and the degree of prosperity of the secondary industry is closely linked. Chinas urban carbon emissions have a significant positive spatial correlation between 2006 and 2016, the cold spot area of urban carbon emissions is relatively stable, mainly distributed in the eastern and southern economic zones, while the hot spot area is mainly distributed in the northwest, northeast and the middle reaches of the Yellow River economic zone.

Household carbon emissions are important components of total carbon emissions. The consumer side of energy-saving emissions reduction is an essential factor in reducing carbon emissions. In this paper, the carbon emissions coefficient method and Consumer Lifestyle Approach (CLA) were used to calculate the total carbon emissions of households in 30 provinces of China from 2006 to 2015, and based on the extended Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, the factors influencing the total carbon emissions of households were analyzed. The results indicated that, first, over the past ten years, the energy and products carbon emissions from China’s households have demonstrated a rapid growth trend and that regional distributions present obvious differences. Second, China’s energy carbon emissions due to household consumption primarily derived from the residents’ consumption of electricity and coal; China’s products household carbon emissions primarily derived from residents’ consumption of the high carbon emission categories: residences, food, transportation and communications. Third, in terms of influencing factors, the number of households in China plays a significant role in the total carbon emissions of China’s households. The ratio of children 0–14 years old and gender ratio (female = 100) are two factors that reflect the demographic structure, have significant effects on the total carbon emissions of China’s households, and are all positive. Gross Domestic Product (GDP) per capita plays a role in boosting the total carbon emissions of China’s households. The effect of the carbon emission intensity on total household carbon emissions is positive. The industrial structure (the proportion of secondary industries’ added value to the regional GDP) has curbed the growth of total carbon emissions from China’s household consumption. The results of this study provide data to support the assessment of the total carbon emissions of China’s households and provide a reasonable reference that the government can use to formulate energy-saving and emission-reduction measures.

The significant spatial heterogeneity among river basin ecosystems makes it difficult for local governments to carry out comprehensive governance for different river basins in a special administrative region spanning multi-river basins. However, there are few studies on the construction of a comprehensive governance mechanism for multi-river basins at the provincial level. To fill this gap, this paper took Henan Province of China, which straddles four river basins, as the study region. The chord diagram, overlay analysis, and carbon emission models were applied to the remote sensing data of land use to analyze the temporal and spatial patterns of carbon storage caused by land-use changes in Henan Province from 1990 to 2018 to reflect the heterogeneity of the contribution of the four basins to human activities and economic development. The results revealed that food security land in the four basins decreased, while production and living land increased. Ecological conservation land was increased over time in the Yangtze River Basin. In addition, the conversion from food security land to production and living land was the common characteristic for the four basins. Carbon emission in Henan increased from 134.46 million tons in 1990 to 553.58 million tons in 2018, while its carbon absorption was relatively stable (1.67–1.69 million tons between 1990 and 2018). The carbon emitted in the Huai River Basin was the main contributor to Henan Province’s total carbon emission. The carbon absorption in Yellow River Basin and Yangtze River Basin had an obvious spatial agglomeration effect. Finally, considering the current need of land spatial planning in China and the goal of carbon neutrality by 2060 set by the Chinese government, we suggested that carbon sequestration capacity should be further strengthened in Yellow River Basin and Yangtze River Basin based on their respective ecological resource advantages. For future development in Hai River Basin and Huai River Basin, coordinating the spatial allocation of urban scale and urban green space to build an ecological city is a key direction to embark upon.

With the rapid development of Chinese economy and increasing improved living standards, the amount of carbon emissions in China has been increasing consistently in a high speed, which consists of the largest percentage of the world’s total carbon emissions in recent years. The construction industry, playing an important role in the Chinese economy, accounts for a large proportion of the total carbon emissions in China. In this paper, the carbon emissions from construction industry in China in 2009 are analyzed by adopting Multi Regional Input-output (MRIO) Model and the World Input-Output Database (WIOD). Results show that, according to the data in 2009, the construction industry is the largest carbon emitter among all industries in China, responsible for the emissions of 2,121,649.31 kt CO2, accounting for 66.54 % of Chinese total carbon emissions. This emission value is contributed by other economic sectors and activities, and it has been found that the industrial sector “Electricity, Gas and Water Supply” is the largest contributor to the carbon emissions of Chinese construction industry, with an amount of 984,830.85 kt CO2, accounting for 46.42 % of the total carbon emissions of Chinese construction industry. Furthermore the carbon emissions in the construction industry comprise 71,418.19 kt CO2 (3.37 %) of direct carbon emissions and 2,050,231.12 kt CO2 (96.63 %) of indirect carbon emissions. The carbon emissions of domestic goods, exports and imports within construction industry are 2,129,974.07, 8663.33 and 338.58 kt CO2, respectively, consisting of 100.39, 0.41 and 0.02 % of the total carbon emissions of Chinese construction industry. The results can help identify critical areas where policymakers can formulate effective policy measures for carbon emissions reduction in Chinese construction industry.

Agriculture is an important part of Henan's economic development, and agricultural production is also an important source of greenhouse gas emissions. In this paper, the activity level data of agricultural carbon emissions in 18 cities in Henan Province were collected. Based on the calculation method provided by the Guidelines for Compiling Provincial Greenhouse Gas Inventories and referring to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories, agricultural CO2, CH4 and N2O emissions were carefully estimated. An agricultural greenhouse gas inventory was compiled for Henan Province from 2011 to 2020. Then, based on the spatial distribution and emission intensity, the carbon emissions of each city in Henan Province in 2019 were further analyzed. The results show that carbon emissions decreased from 57,603,500 tons in 2011 to 44,517,300 tons in 2020, with an average annual decrease rate of about 2.66%. The total agricultural carbon emissions in Henan showed a downward trend, and agricultural land was the dominant factor of carbon emissions, accounting for about 47% of the total carbon emissions over the years.

The decoupling relationship between China’s economic growth and carbon emissions from the perspective of industrial structure

Summary Cities, contributing more than 75% of global carbon emissions, are at the heart of climate change mitigation. Given cities' heterogeneity, they need specific low-carbon roadmaps instead of one-size-fits-all approaches. Here, we present the most detailed and up-to-date accounts of CO2 emissions for 294 cities in China and examine the extent to which their economic growth was decoupled from emissions. Results show that from 2005 to 2015, only 11% of cities exhibited strong decoupling, whereas 65.6% showed weak decoupling, and 23.4% showed no decoupling. We attribute the economic-emission decoupling in cities to several socioeconomic factors (i.e., structure and size of the economy, emission intensity, and population size) and find that the decline in emission intensity via improvement in production and carbon efficiency (e.g., decarbonizing the energy mix via building a renewable energy system) is the most important one. The experience and status quo of carbon emissions and emission-GDP (gross domestic product) decoupling in Chinese cities may have implications for other developing economies to design low-carbon development pathways.

Carbon emission reduction is an important part of regional low-carbon economic development. In this paper, gray correlation analysis, neural network model, Gaussian multi-mode fitting and other methods were used to analyze the relationship between total carbon emissions and regional economic development, industrial structure, and energy consumption in Henan Province. On this basis, the future development of carbon emissions is predicted. The calculation results showed that the correlation between the three industries and carbon emissions in Henan Province is more than .7, among which the secondary industry has the highest correlation (.77). In the secondary industry, the correlation coefficient between coal and carbon emissions is the highest .87, while the correlation coefficient between other energy sources is about .5. In the neural network prediction model, the correlation coefficient between the prediction curve and the actual total carbon emission curve is .989, and the prediction results have a good degree of fit. The carbon emission prediction curve was divided into two parts: a linear decline stage from 2018 to 2024, and a rapid decline stage after 2024.The results showed that more efforts should be made in industrial structure, energy consumption structure and environmental protection to achieve low-carbon development in Henan province.

This study examines the nexus between business strategy and carbon emissions by utilising a dataset of U.S. firms from 2007 to 2020. It focuses on two broad types of firms, that is, prospectors and defenders. Regarding carbon emissions, we consider total emissions (Scope 1 & 2), direct emissions (Scope 1) and indirect emissions (Scope 2). The results reveal a significant association between business strategy and total carbon emissions as well as direct carbon emissions. Notably, the results suggest that prospectors, compared to defenders, display higher levels of total and direct carbon emissions. Our findings contribute to the debate on whether prospectors in developed countries mismanage sustainability issues. The study offers valuable insights into the interplay between business strategy and carbon emissions and provides empirical evidence that business strategy is an important determinant of total and direct carbon emissions.

China is undergoing rapid urbanization, enlarging the construction industry, greatly expanding built-up land, and generating substantial carbon emissions. We calculated both the direct and indirect carbon emissions from energy consumption (anthropogenic emissions) in the construction sector and analyzed built-up land expansion and carbon storage losses from the terrestrial ecosystem. According to our study, the total anthropogenic carbon emissions from the construction sector increased from 3,905×10(4) to 103,721.17×10(4) t from 1995 to 2010, representing 27.87%-34.31% of the total carbon emissions from energy consumption in China. Indirect carbon emissions from other industrial sectors induced by the construction sector represented approximately 97% of the total anthropogenic carbon emissions of the sector. These emissions were mainly concentrated in seven upstream industry sectors. Based on our assumptions, built-up land expansion caused 3704.84×10(4) t of carbon storage loss from vegetation between 1995 and 2010. Cropland was the main built-up land expansion type across all regions. The study shows great regional differences. Coastal regions showed dramatic built-up land expansion, greater carbon storage losses from vegetation, and greater anthropogenic carbon emissions. These regional differences were the most obvious in East China followed by Midsouth China. These regions are under pressure for strong carbon emissions reduction.

Land use, as one of the major sources of carbon emissions, has profound implications for global climate change. County-level land-use systems play a critical role in national carbon emission management and control. Consequently, it is essential to explore the spatiotemporal effects and optimization strategies of land-use carbon emissions at the county scale to promote the achievement of regional dual carbon targets. This study, focusing on Shaanxi Province, analyzed the spatiotemporal characteristics of land use from 2000 to 2020. By establishing a carbon emission evaluation model, the spatiotemporal effects of county-level carbon emissions were clarified. Utilizing Geodetector and K-means clustering methods, the driving mechanisms and clustering characteristics of county-level carbon emissions were elucidated, and optimization strategies for land use carbon emission were explored. The results showed that during 2000–2020, land use in Shaanxi Province underwent significant spatiotemporal changes, with constructed land increasing by 97.62%, while cultivated land and grassland were substantially reduced. The overall county-level carbon emissions exhibited a pattern of North > Central > South. The total carbon emissions within the province increased nearly fourfold over 20 years, reaching 1.00 × 108 tons. Constructed land was the primary source of emissions, while forest land contributed significantly to the carbon sink of the study area. Interactions among factors had significant impacts on the spatial differentiation of total county-level carbon emissions. For counties with different types of carbon emissions, differentiated optimization strategies were recommended. Low-carbon emission counties should intensify ecological protection and rational utilization, medium-carbon emission counties need to strike a balance between economic development and environmental protection, while high-carbon emission counties should prioritize profound emission reduction and structural transformation.

In recent years, the environmental problems caused by excessive carbon emissions from energy sources have become increasingly serious, which not only aggravates the climate change caused by the greenhouse effect but also seriously restricts the sustainable development of Chinese economy. An attempt is made in this paper to use energy consumption method and input-output method to study the carbon emission structure of China's energy system and industry in 2015 from two perspectives, namely China's energy supply side and energy demand side, by taking into account the two factors of energy invest in gross capital formation and export. The results show that neglecting these two factors will lead to underestimation of intermediate use carbon emissions and overestimation of final use carbon emissions. On energy supply side, the carbon emission structure of China's energy system is still dominated by high-carbon energy (raw coal, coke, diesel, and fuel oil, etc.), accounting for more than 70% of total energy carbon emissions; on the contrary, the natural gas such as clean energy accounts for only 3.45% of total energy carbon emissions, indicating that the energy consumption structure optimization and emission reduction gap of China's energy supply side are still substantial. On energy demand side, the final use (direct consumption by residents and government) produces less carbon emissions, while the intermediate use (production by enterprises) produces more than 90% of the total energy carbon emissions. Fossil energy, power sector, heavy industry, chemical industry, and transportation belong to industries with larger carbon emissions and lower carbon emission efficiency, while agriculture, construction, light industry, and service belong to industries with fewer carbon emissions and higher carbon emission efficiency. This means that the optimization of industrial structure is conducive to slowing down the growth of energy carbon emissions on the demand side.