Carbon dioxide emissions, urbanization level, and industrial structure: empirical evidence from North China

A significance of smart city pilot policies in China for enhancing carbon emission efficiency in construction

The Chinese government seeks to promote economic growth and sustainable development while achieving carbon neutrality by establishing phased smart city pilots. Therefore, it is important to study whether smart city pilots can promote carbon emission efficiency (CEE). This paper constructs a multi-period difference-in-difference (DID) model based on panel data from 241 prefecture-level cities in China from 2007 to 2019, aiming to investigate the mechanism of the impact of smart city pilot policies (SCPP) on CEE and whether there is a rebound effect. The study found that smart city construction (SCC) significantly improves carbon efficiency, with pilot cities increasing their CEE by 1.4% compared to non-pilot cities. The conclusions remain robust under a variety of scenarios including the introduction of placebo tests, counterfactual tests, sample data screening, and omitted variable tests. The results of the mechanism test show that although the rebound effect can inhibit the improvement of CEE, the environment can be improved and the CEE can be enhanced through green technology innovation, industrial structure upgrading, energy structure optimization, environmental regulation effect, information technology support, and resource allocation effect. The heterogeneity results indicate that the SCPP is more effective in promoting CEE in cities in the eastern region, southern cities, environmentally friendly cities, large cities, and medium-sized cities. This study contributes to the existing literature in clarifying the environmental benefits of SCPP and provides valuable policy insights for cities to address climate change and sustainable development.

Carbon emissions prediction considering environment protection investment of 30 provinces in China

In the backdrop of carbon peaking and carbon neutrality, carbon emissions have always been a major concern. The approach of the heterogeneity grey model is proposed, aiming to predict carbon emissions of 30 provinces in China. This model combines the manta ray foraging optimization algorithm to search for the optimal heterogeneity coefficient. By using the heterogeneity grey model, the carbon emissions are analyzed in 30 provinces of China from 2022 to 2030 considering different environmental protection investment scenarios. The results indicate that in 19 provinces from 2022 to 2030, there is a significant decrease in carbon emissions as government investment increases. In 11 provinces during the same period, there is a rising trend in carbon emissions with the increase of government investment. Hence, achieving a reduction in carbon emissions necessitates not only relying on government investment in environmental protection but also exploring alternative approaches to mitigate carbon emissions. The methodologies and conclusions proposed in this study can provide technical references and making decision references for provincial carbon emission efforts.

Impact of digital finance on urban ecological resilience: evidence from the Yangtze River Economic Belt in China

From the emergence of the new coronavirus pandemic to extreme climatic catastrophes, the development and enhancement of urban ecological resilience has evolved into a critical and strategic imperative. Investigating the capacity of digital finance to promote urban ecological resilience bears substantial relevance to the sustainable advancement of urban centers. This study examines the influence of digital finance on urban ecological resilience by applying a benchmark regression model on data from 107 prefecture-level cities within the Yangtze River Economic Belt across 2011-2020. Additionally, this study delves into its mechanism and spatial spillover impacts via a mediating effect model and a spatial effect model. The findings revealed that (1) digital finance strengthens the ecological resilience of the locale and beneficially impacts the surrounding regions; (2) digital finance enhances urban ecological resilience by fostering technological innovation and reducing energy intensity; and (3) in the lower reaches of the Yangtze River, digital finance plays a greater role in improving urban ecological resilience. Cities with high level of traditional financial development, high level of economic development and high intensity of environmental regulation have a more obvious role in promoting urban ecological resilience. Within the paradigm of ecological civilization, it is advisable for governmental bodies to fortify inter-regional digital financial collaboration, refine the green financial infrastructure, and advocate for sustainable, low-carbon, high-quality urban development.

Impact of energy and industrial structure on environmental quality and urbanization: evidence from a panel of BRICS countries

Global sustainable development demands boosting renewable energy and optimizing industrial structures. This study employs a panel vector autoregressive (PVAR) model to examine the dynamic relationship between energy structure, industrial structure, environmental quality, and urbanization in the BRICS countries from 1990 to 2021. Energy structure, industrial structure, environmental quality, and urbanization cointegrate empirically. Energy mix optimization and industrial structure upgrades can improve environmental quality. Energy enhancements also supported urbanization. Accelerating industrial change could adversely impact urbanization. The impulse response results demonstrate that expanding renewable energy and tertiary industries such as financial and service boost environmental quality and urbanization. The variance decomposition investigation reveals significant "path dependence" in reducing carbon emissions and increasing urbanization. Finally, based on the findings, policy insights for enhancing environmental quality and fostering urbanization are laid out and disputed, with long-term implications for environmental managers and urban planners.

Regional common prosperity level and its spatial relationship with carbon emission intensity in China

The characteristics of common prosperity include harmonious relationships between humans and the environment, as well as sustainable economic and social growth. The process of achieving common prosperity will necessarily have an impact on carbon emissions. In this article, panel statistics collected from 30 Chinese provinces and cities between the years 2006 and 2020 are utilized to assess the level of common prosperity and the intensity of carbon emissions in China. Then the SDM model is applied to explore the effects of the common prosperity level on the intensity of carbon emissions. The findings reveal that: (i) The common prosperity level in China has shown an increasing tendency. Between 2006 and 2020, the mean level of common prosperity increased from 0.254 to 0.486. From the regional perspective, eastern China has seen greater levels of common prosperity than central China, while central China has experienced greater levels of common prosperity than western China; regional disparities in the degree of common prosperity are substantial among Chinese provinces from 2006 to 2020; the common prosperity level is relatively high in economically developed provinces and relatively low in economically backward provinces. (ii) China's carbon emission intensity shows a continuous downward tendency. The annual average intensity of China's carbon emissions decreased from 4.458 in 2006 to 2.234 in 2020. From the regional perspective, the three main regions' carbon emission intensity likewise exhibits a decline in tendency between 2006 and 2020; still, western China continues to have the greatest carbon emission intensity, following central China, while eastern China has the smallest; however, certain provinces, notably Inner Mongolia and Shanxi, continue to have high carbon emission intensity. (iii) China's common prosperity level and carbon emission intensity both exhibit positive spatial autocorrelation at a 1% significant level under the adjacency matrix. The spatial agglomeration effect is significant, and adjacent provinces can affect each other. (iv) The SDM (Spatial Durbin Model) model test with fixed effects finds that the increase in the level of common prosperity suppresses the intensity of carbon emissions in the local area and neighboring regions. (v) The mediating effects model indicates that the process of common prosperity suppresses carbon emission intensity through high-quality economic development, narrowing the income disparity, and the development of a sharing economy.

Reducing agricultural nitrous oxide emissions in China: the role of food production, forest cover, income, trade openness, and rural population

In the light of China's carbon-neutral goal, this study examines how food production, forest cover, trade openness, and rural population contribute to the quest of addressing China's agricultural nitrous oxide emissions. Time series data ranging from 1971 to 2018 was used for analysis in this study. The autoregressive distributed lag (ARDL) technique was employed to evaluate potential cointegration as well as to ascertain the long and short-run effects of food production, forest cover, income, trade openness, and rural population on agricultural nitrous oxide emission. The Toda-Yamomoto causality analysis was also used to identify the causal relations between covariates (food production, forest cover, income, trade openness, and rural population) and the outcome variable (agricultural nitrous oxide emission). The long-run evidence is that rural population in itself tends to increase agricultural nitrous oxide emissions likewise food production. There is also validation of the existence of environmental Kuznets curve for agricultural nitrous oxide emissions. Moreover, income interacts with rural population to reduce agricultural nitrous oxide emissions in the long-run. Causality analysis indicated rural population affects the level of forest cover; forest cover is found to cause agricultural nitrous oxide emissions but the converse is not established, and income as well as the interaction between income and rural population determines agricultural nitrous oxide emissions. The short-run dynamics results establish an oscillatory equilibrium convergence for agricultural nitrous oxide emissions in event of structural disturbances. From the findings, the EKC hypothesis is relevant by offering avenue to reduce emission. Thus, income growth remains helpful in addressing nitrous oxide emission from the agricultural sector. However, research is needed to unravel why nitrous oxide tends to increase in many forest areas. Since food production cannot be halted, policy makers need to enhance the uptake of efficient food production technologies including developing and using more renewable energy for food production. It is important for authorities to attend to rural development in order to mitigate agricultural nitrous oxide emissions in China.

Heading towards sustainable environment: does renewable and non-renewable energy generation matter for the effect of industrialization and urbanization on ecological footprint? Evidence from China

This study examines how renewable and non-renewable energy generation interacts with both to affect the ecological footprint in China during 1990-2019 by using FMOLS, DOLS, and CCR estimation techniques and ARDL simulation models to assess the impact of industrialization and urbanization on environmental sustainability based on the environmental Kuznets curve hypothesis model framework. Firstly, the findings verify the applicability and validity of the EKC hypothesis in China. Secondly, renewable energy generation, industrialization, and urbanization facilitate the reduction of ecological footprint and the improvement of environmental quality in the long run, while non-renewable energy generation increases the ecological footprint and leads to the intensification of ecological pollution. However, the short-term estimates give evidence that industrialization, urbanization, and renewable and non-renewable energy generation can all increase the ecological footprint, which is not conducive to ecological sustainability. Thus, from the perspective of ecological sustainability in China, our findings are important in that they provide clear directions for ecological policy formulation, and we also provide some targeted policy recommendations for them to promote sustainable development as a goal.

Heterogeneous impacts of multi-energy power generation on carbon emissions: evidence from China's provincial data

The goals of carbon peak and carbon neutrality have put forward higher requirements for the low-carbon development of power supply. This paper discusses the impacts of multi-energy power generation on carbon emissions for 30 regions in China and proposes low-carbon development suggestions for the electric power industry. The research found that firstly there is a significant strong positive correlation between thermal power and carbon emissions in most regions of China, so the carbon emission reduction of power supply should still focus on thermal power. Secondly, wind power and photovoltaic power have positive effects or negative effects on carbon emissions in different regions. But combined with the analysis results in regions with the rapid development of wind power or photovoltaic power, it could be found that wind power and photovoltaic power contributed to reducing carbon emissions when they developed to a certain scale. It is proposed to speed up the construction of wind power and photovoltaic power in regions with rich wind resources or solar resources such as Inner Mongolia, Xinjiang, Liaoning, and Gansu. Thirdly, hydropower and nuclear power both have negative effects on carbon emissions. Considering the large demand for electricity in coastal regions where nuclear power is located, it is suggested that coastal regions should gradually promote the construction and application of nuclear power on the basis of safety.

Impact of digital economy development on carbon emission intensity in the Beijing-Tianjin-Hebei region: a mechanism analysis based on industrial structure optimization and green innovation

Under the "Digital China" strategy and "Carbon Peaking and Carbon Neutrality" goal, it is significant to explore the carbon reduction effect from the digital economy development in a multi-dimensional way. Based on the panel data of 13 cities in the Beijing-Tianjin-Hebei (BTH) region from 2011 to 2019, this study uses mechanism test model, threshold effect model, and spatial Durbin model which empirically test the influence mechanism and spatial spillover effect of digital economy development on regional CEI. The research found that (1) the digital economy development in the BTH region can reduce regional CEI, and it passes the endogenous test; (2) the digital economy indexes of 13 cities in the BTH region have significantly increased with time evolution, but there is obvious spatial unevenness; the CEI of each city except Tianjin decreases significantly with time evolution, and Tianjin shows a trend of decreasing and then increasing; (3) digital economy has a positive spatial correlation, showing the characteristics of "H-H" and "L-L" clustering. Furthermore, the digital economy has a spatial spillover effect on the CEI of neighboring cities; (4) the digital economy development can promote the industrial structure rationalization and upgrade, improves the urban green innovation quantity and quality, then reduces the regional CEI through them; and (5) the impact strength of digital economy on CEI varies at different threshold intervals of the mechanism variable.

Urban Expansion and Carbon Emission Logistic Curve Hypothesis and Its Verification: A Case Study of Jiangsu Province

Based on the general patterns of urban expansion and carbon emissions at home and abroad, the IPCC carbon emission coefficient estimation method and logistic equation are applied to verify the logistic curve relationship between urban expansion and carbon emissions and to quantitatively measure the upper limit of carbon emissions and the inflection point of carbon emission growth. The results show that (1) the corresponding cumulative carbon emission intensity of foreign (regional) urban expansion gradually decreases during the transition from the primary stage to the saturation stage; (2) urban expansion and carbon emissions in China are characterized by cyclical fluctuations during the 1978–2014 period, and the fluctuations of the two show significant decoupling or divergence after 2014; and (3) urban expansion and carbon emission in Jiangsu province during the 2002–2019 period shows a logistic curve hypothesis relationship, and the cumulative carbon emissions in the built-up areas of Southern Jiangsu, Central Jiangsu and Northern Jiangsu show an inflection point when they reach 3128.12 km2, 627.25 km2 and 973.9 km2, with the cumulative carbon emission caps of 197.238 × 108 t, 14.487 × 108 t and 29.289 × 108 t, respectively.

The Impact of Rationalization and Upgrading of Industrial Structure on Carbon Emissions in the Beijing-Tianjin-Hebei Urban Agglomeration

Carbon dioxide mainly comes from industrial economic activities. Industrial structure optimization is an effective way to reduce carbon dioxide emissions. This paper uses the panel data of 13 cities in the Beijing-Tianjin-Hebei urban agglomeration from 2006 to 2019, uses the Theil index to calculate the industrial structure rationalization index, and uses the proportion of industrial added value to calculate the industrial structure upgrade index. By constructing the STIRPAT model, this paper quantitatively analyzes the impact of industrial structure rationalization and upgrade on carbon emissions. The results show that the rationalization and upgrading of industrial structure in the Beijing-Tianjin-Hebei urban agglomeration significantly inhibit carbon emissions. Compared with the rationalization of the industrial structure, the upgrading of industrial structure in the Beijing-Tianjin-Hebei urban agglomeration has a better effect on carbon emission reduction. For the Beijing-Tianjin-Hebei urban agglomeration, government expenditure on science and technology can promote the upgrading of industrial structure to a certain extent, thereby reducing carbon emissions. There is a big gap between the industrial structure development level of Hebei province and that of Beijing and Tianjin. Finally, based on the conclusion, this paper puts forward the policy enlightenment of promoting the optimization process of industrial structure and reducing carbon emissions of the Beijing-Tianjin-Hebei urban agglomeration.