Accelerating decarbonized economic growth through net-zero entrepreneurship: Low-carbon economic development perspective.

Net-zero entrepreneurship is a novel concept introduced in the context of carbon neutrality, and exploring whether it can catalyze decarbonized economic growth is a worthy pursuit. This study constructs a comprehensive, low-carbon endogenous economic growth model to scrutinize the intricate nexus between net-zero entrepreneurship and decarbonized economic growth. Empirical validation employs a series of multiple regression models to rigorously test the hypotheses derived from the theoretical framework using an extensive dataset spanning Chinese provinces. The results reveal a nuanced landscape. (i) Net-zero entrepreneurship plays a remarkable role in promoting decarbonization growth, with considerable regional heterogeneity. (ii) Green technology progress exhibits a notable mediating effect. (iii) Environmental regulation and industrial structure optimization have positive moderating effects. (iv) The results passed alternative dependent variable and one-phase lag regression robustness tests. In a distinct contribution to entrepreneurship literature, this study augments the discourse on strategies to steer low-carbon transitions. The research findings indicate that net-zero entrepreneurship can accelerate the global decarbonization process, and green technology progress is a significant driving mechanism in this process. Additionally, it is essential to strengthen environmental agencies' regulatory oversight and optimize industrial structures to pave the way for transformative sustainable growth. In the future, more entrepreneurs should be encouraged to engage in green technology and business model innovation to contribute to global decarbonization efforts.

Urban synergistic carbon emissions reduction research: A perspective on spatial complexity and link prediction.

Reducing urban carbon emissions (UCEs) holds paramount importance for global sustainable development. However, the complexity of interactions among urban spatial units has impeded further research on UCEs. This study investigates synergistic emission reduction between cities by analyzing the spatial complexity within the UCEs network. The future potential for synergistic carbon emissions reduction is predicted by the link prediction algorithm. A case study conducted in the Pearl River Basin of China demonstrates that the UCEs network has a complex spatial structure, and the synergistic capacity of emission reduction among cities is enhanced. The core cities in the UCEs network, including Dongguan, Shenzhen, and Guangzhou, have spillover effects that contribute to synergistic emission reduction. Community detection reveals that the common characteristics associated with UCEs become concentrated, thereby enhancing the synergy of joint efforts between cities. The link prediction algorithm indicates a high probability of strengthened carbon emission connections in the Pearl River Delta, alongside those between upstream cities, which shows potential in forecasting synergistic emission reductions. Our research framework offers a comprehensive analysis for synergistic emission reduction from the spatial complexity of UCEs network and link prediction. It acts as a worthwhile reference for developing differentiated policies on synergistic emission reduction.

Evaluating carbon emissions reduction compliance based on 'dual control' policies of energy consumption and carbon emissions in China.

Dual control policies aim to effectively reduce energy consumption and carbon emission by setting targets for total volume and intensity control. With global climate change becoming increasingly serious. China, as a large energy-consuming and carbon-emitting country, faces a huge challenge to reduce carbon emissions. The transition from the "dual control of energy consumption" to the "dual control of carbon emissions" in China is necessary to optimize energy structures, promote energy conservation, reduce carbon emissions, and reach carbon neutrality. This study utilizes multi-regional input-output models to evaluate the results toward both goals from production-based, consumption-based, and income-based perspectives. The findings indicate that "dual control of carbon emissions" is a more precise approach than "dual control of energy consumption". Some provinces have met the latter goal while still falling short of the former. Provinces having met their production-based energy intensity and carbon emission intensity targets, but having failed their consumption-based and income-based targets, are identified to develop a comprehensive and accurate assessment of these targets. A net outflow of embodied energy and carbon emissions is observed among provinces in less-developed central and southern regions to more-developed eastern and northern regions. Suggestions and policy implications based on these findings include establishing a comprehensive evaluation of energy and carbon intensity, considering both consumption-based and income-based perspectives, as well as facilitating enhanced cooperation among developed and developing provinces.

Assessment of site suitability for centralized photovoltaic power stations in Northwest China's six provinces.

Northwest China has abundant solar energy resources and extensive land, making it a pivotal site for solar energy development. However, restrictions on site selection and severe weather conditions have hindered the establishment and operation of photovoltaic (PV) power stations. Previous studies have not considered meteorological factors when evaluating site suitability, leading to research gaps in identifying suitable areas and establishing indicator systems. We aimed to address these gaps by considering seven factors constraining the construction of centralized PV power stations (CPPS) and developing an indicator system based on terrain, climate, soil, and economic factors. Furthermore, we conducted analyses to quantify the solar energy generation potential (SEGP), carbon emissions reduction benefits, and land utilization potential at different sites. The findings indicate that areas rated as very suitable and extremely suitable comprised the largest proportion (62.35%) of site suitability. The correlation between site suitability and electricity consumption was largely non-significant, highlighting the need for enhanced coordination. Additionally, we forecast the electricity consumption in Xinjiang, Gansu, Inner Mongolia, Qinghai, Ningxia, and Shaanxi for 2030 to be 56.62, 19.86, 54.54, 13.59, 15.96, and 33.34 ( × 1011 kWh), respectively, with corresponding carbon emissions reduction potentials of 20.2, 7.1, 19.4, 4.8, 5.7, and 11.9 ( × 109 kg). Consequently, PV carbon reduction and land utilization potential are substantial.

Research on Carbon Emissions Reduction Strategy Considering Government Subsidy and Free Riding Behavior.

Government subsidy can greatly encourage supply chain enterprises to reduce carbon emissions. To quickly occupy the market, supply chain enterprises form alliances. However, enterprises in the alliance have speculative psychology, and the impact of such free riding behavior on the carbon emissions reduction willingness of supply chain enterprises is still unclear. In this article, government subsidies and free riding behavior parameters are introduced to build a carbon emissions reduction decision model for the government, manufacturers, and suppliers, and the impact of government subsidies and free riding behavior on the decision making of supply chain enterprises is analyzed through evolutionary game theory. The analysis shows that government subsidies have an incentive effect on carbon emissions reduction of supply chain enterprises. After the market stabilizes, even if the government subsidies are gradually withdrawn, the carbon emissions reduction of supply chain enterprises still converges to Pareto optimal equilibrium. The influence of free riding behavior on supply chain enterprises depends on the carbon emissions reduction profit. When the carbon emissions reduction profit is different, the decision of manufacturers and suppliers will be different. The above conclusions provide a reference for governments to strengthen control or enterprises to make decisions on carbon emissions reduction.

Does the development of renewable energy promote carbon reduction? Evidence from Chinese provinces.

Developing renewable energy is a crucial measure in addressing climate change and achieving carbon reduction. However, research evidence on its impact is mixed. To fill this gap, we construct a panel quantile regression model in this study to examine whether China's renewable energy development has effectively promoted a reduction in carbon emissions using panel data of 30 Chinese provinces from 2005 to 2016. The results show that: (1) Improving China's renewable energy development level is conducive to carbon emission reductions. Specifically, carbon intensity could drop by 0.084%-0.149% for every 1% increase in renewable energy generation. However, the inhibitory effect is limited due to trapped electricity as well as the fact that substituting renewable energy for fossil energy has not yet sufficiently transformed the energy consumption structure. (2) Renewable energy development has a greater impact on carbon intensity reduction in regions with high or low carbon intensity than in areas with intermediate carbon intensity. (3) The main factor in the decline in carbon intensity in China is a decrease in energy intensity. Nonetheless, the role of renewable energy in carbon reduction has increased over time.

Firms' shareholding behavior in green supply chains: Carbon emissions reduction, power structures, and technology spillovers.

Leaders in green supply chains are increasingly focusing on improving strategic synergy with followers through shareholding strategies. By constructing Stackelberg models, we explore the operational mechanisms in two models, manufacturer-led and retailer-led, which have forward and backward shareholding strategies, respectively. Compared with non-shareholding models, we find that the retailer's pricing becomes more sensitive to changes in the market environment after applying shareholding strategies, while the manufacturer's pricing depends on its power status. Interestingly, leaders and entire supply chains prefer shareholding strategies, while followers prefer shareholding strategies in good market environment or in bad market environment with their shares held by leaders below certain thresholds. Moreover, both forward and backward shareholding strategies can effectively promote carbon emissions reduction. Improving manufacturers' technology spillover positively impacts pricing and carbon emissions reduction and profits, and a reasonable shareholding ratio can encourage manufacturers to increase the level of technology spillover. Finally, a two-part tariff contract can effectively coordinate the vertical shareholding supply chain. The results provide decision guidance for managers in applying shareholding strategies to build a strategic alliance to improve firms' economic and environmental performance.

Are hybrid approaches combining EKF-UKF and artificial neural networks key to unlocking the full potential of sustainable energy technologies and reducing environmental footprint?

The integration of traditional state estimation techniques like the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) with modern artificial neural networks (ANNs) presents a promising avenue for advancing state estimation in sustainable energy systems. This study explores the potential of hybridizing EKF-UKF with ANNs to optimize renewable energy integration and mitigate environmental impact. Through comprehensive experimentation and analysis, significant improvements in state estimation accuracy and sustainability metrics are revealed. The results indicate a substantial 8.02 % reduction in estimation error compared to standalone EKF and UKF methods, highlighting the enhanced predictive capabilities of the hybrid approach. Moreover, the integration of ANNs facilitated a 12.52 % increase in renewable energy utilization efficiency, leading to a notable 5.14 % decrease in carbon emissions. These compelling outcomes underscore the critical role of hybrid approaches in maximizing the efficiency of sustainable energy technologies while simultaneously reducing environmental footprint. By harnessing the synergies between traditional filtering techniques and machine learning algorithms, hybrid EKF-UKF with ANNs emerges as a key enabler in accelerating the transition towards a more sustainable and resilient energy landscape.

The synergistic effect of digital economy and manufacturing structure upgrading on carbon emissions reduction: Evidence from China.

Promoting the integration of the digital economy with the manufacturing-based real economy is beneficial to avoid the detachment of economic development from tangible industries. Whether the low-carbon transformation can be achieved in this integration process is also an important issue. So, taking China for instance, we theoretically analyze the impact mechanism of the integration of the digital economy with three major categories of manufacturing (labor-intensive, capital-intensive, and technology-intensive) on carbon emissions, and empirically test those effects based on 30 provinces in China from 2011 to 2019. The following conclusions are drawn: (1) The development of the digital economy can reduce carbon emissions. (2) The integration of the digital economy with different categories within the manufacturing industry causes different effects on carbon emissions reduction, shown as a structural upgrading type of carbon emissions reduction, i.e., the deeper integration between digital economy and technology-intensive manufacturing contributes to a multiplier effect in carbon emissions reduction. (3) The efficiency improvements benefited from the integration with the digital economy in technology-intensive manufacturing are the main reason for the structural upgrading type of carbon emissions reduction. Therefore, policy should aim at accelerating the integration of the digital economy with advanced manufacturing to realize comprehensive low-carbon transformation.

Spatial effects and heterogeneity analysis of the impact of environmental taxes on carbon emissions in China.

Environmental taxes are important means by which governments can address environmental pollution problems. Amid increasingly severe global warming, how should environmental taxes be used to better combat pollution and reduce emissions to promote sustainable socioeconomic development? This empirical analysis explores the influence of environmental taxes on CO2 emissions by utilizing a spatial Durbin model constructed with panel data from 2006 to 2020 encompassing 30 provinces, autonomous regions, and municipalities under the direct jurisdiction of China's central government. First, we found that a strong spatial auto-correlation exists between carbon emission intensity and environmental taxes at the geographic and economic levels in each province. The characteristics of high-high and low-low agglomeration are consistent with the actual situation where each province has a strong regional correlation. Second, the estimation results of environmental taxes' spatial effect on carbon emissions show that under the neighboring space weight matrix, environmental taxes and fees can not only better promote regional carbon emission reduction but also reduce the carbon emissions of neighboring regions. Under the economic distance weight matrix, environmental taxes' impact on reducing carbon emission intensity in the province is not significant, but it can promote the reduction of carbon emissions in the economically neighboring provinces. Additionally, the results of the sub-tax estimation of environmental taxes and carbon emission intensity show that differences exist in the impacts of different environmental taxes on carbon emission intensity under different weight matrices. Among them, environmental protection, resource, vehicle and vessel, and urban land use taxes are basically unfavorable in reducing carbon emission intensity in a region and its neighboring regions, while urban maintenance and construction and cultivated land occupation taxes enhance carbon emission reduction. Our findings suggest that efficiently promoting carbon emissions reduction requires effectively utilizing the spatial effects of environmental taxes and carbon emissions, establishing and improving the regional carbon emissions reduction linkage mechanism, including carbon dioxide in the scope of taxation to further strengthen environmental taxes' positive impact on carbon emission reduction, and focusing on the heterogeneity of environmental tax implementation to achieve emission reduction goals.

Unbiasing the estimate of the role of income in carbon footprint of households: Analysis of the Spanish case as a pilot study.

Although the estimation of the elasticity of the household carbon footprint and income is a frequently analysed fact, unfortunately a fundamental aspect of this relationship has not been considered: it is not a constant factor for the whole population. To make an adequate estimate of this relationship, a Quantile Regression is proposed, obtaining significantly different results to those derived from the usual estimations using ordinary least squares (OLS), which have been carried out up to now. This fact is fundamental for the correct planning and evaluation of fiscal policies based on income taxation to reduce the carbon footprint. Our results confirm that the OLS estimation would overestimate the effects of income on CO2 reduction by 26%.

Can economic growth and carbon emissions reduction be owned: evidence from the convergence of digital services and manufacturing in China.

Under the background of the deep convergence of China's digital services and manufacturing, it is of great significance to investigate the effects of the convergence of digital services and manufacturing on economic growth and carbon emissions reduction to the application of digital technology in the whole world. This paper constructs a simultaneous equation model and uses three-stage least squares to estimate the effect and mechanism of industrial convergence on economic growth and carbon emissions. The results show that (i) industrial convergence improves the change of total factor productivity (TFP) and the change of technical efficiency, and the reduction of carbon emissions is the main factor driving the growth of TFP and technical efficiency; (ii) industrial convergence and carbon emissions show a significant U-shaped relationship; (iii) the heterogeneity analysis shows that the convergence of capital-intensive, technology-intensive and labor-intensive manufacturing with digital services will help to improve the growth of TFP, it can inhibit carbon emissions first and then promote it. Therefore, the government should take targeted measures to promote industrial convergence of digital services and manufacturing according to the economic development and industry characteristics, so as to give full play to its positive role in economic growth and emissions reduction.

Analysis of the Coupling Effect and Space-Time Difference between China's Digital Economy Development and Carbon Emissions Reduction.

Previously conducted studies have established that the digital economy has a one-way inhibition effect on carbon emissions. Against this background, this paper aims to analyze the coordinated development effect of the interaction between the digital economy and carbon emissions reduction. The entropy weight method, coupling and coordination degree model, Dagum Gini coefficient and Moran's I index have been carried out as research methods in this paper. The results showed that: (1) The coupling and coordination of China's digital economy and carbon emissions reduction shows an overall growth trend, but the coupling and coordination among regions, provinces and cities show a large imbalance. (2) In the sample period, the overall difference in the coupling and coordination between digital economy development and carbon emissions reduction shows an expanding trend, and the overall difference results are attributed to regional differences. (3) There is a significant spatial correlation in the coupling and coordination degree of digital economy development and carbon emissions reduction among cities. The paper systematically grasps the status of coupling and coordination development, the source of difference and spatial correlation between the digital economy and carbon reduction in Chinese cities. A dependence relationship has been established, which is digital economy development and carbon emissions reduction, and an interactive promotion pattern has been revealed between the digital economic system and the carbon emissions reduction system.

Low-carbon technology service mode with revenue-sharing contract considering advance funding risk.

This study aims to effectively reduce carbon emissions by selecting the low-carbon technology service emission reduction modes. This paper constructs a revenue-sharing contract model based on game theory. The purpose of this paper is to analyze the choice of carbon emission reduction strategies for carbon emission-dependent manufacturers and low-carbon technology service providers with advance funding risk aversion. This study analyzes the effects on the optimal decision-making and profits of both parties of the degree of risk aversion and the investment cost coefficient of carbon emissions reduction for service providers, and initial carbon emissions of manufacturers. The optimal carbon emissions reduction strategy of manufacturers and service providers with numerical analysis is obtained, and the revenue-sharing contract coordination is realized. Finally, the validity of the contract is verified by simulation analysis. The results show that manufacturers and service providers can only achieve optimal cooperation for emission reduction within a certain range of parameters. With different investment cost coefficients of carbon emissions reduction and with different initial carbon emissions, manufacturers will adjust the fixed emissions reduction fee and the revenue-sharing coefficient to encourage service providers to offer the optimal carbon emissions reduction rate. This study enriches theoretical research on low-carbon service chains. It also provides important practical evidence to help manufacturers and service providers choose optimal strategies for coordinating revenue-sharing contracts.

Is Emissions Trading Scheme (ETS) an Effective Market-Incentivized Environmental Regulation Policy? Evidence from China's Eight ETS Pilots.

Climate change and environmental issues caused by carbon emissions have attracted the attention of governments around the world. Drawing on the experience of the EU, China is actively developing a national carbon emissions trading market, trying to encourage emission entities to incorporate carbon emissions reduction into production and consumption decisions through carbon pricing. Is this scheme an effective market-incentivized environmental regulatory policy? Since China successively launched ETS pilots in 2013, the effectiveness of reducing carbon emissions has become one of the current focus issues. This study uses the difference-in-differences (DID) method to evaluate the impact of ETS implementation on emissions reduction and employs the Super-SBM model in data envelopment analysis (DEA) to evaluate the emission-reduction efficiency of eight ETS pilots in China. We find that the carbon trading policy has achieved emission-reduction effects in the implementation stage, and the greenness of economic growth has a significant positive impact on regional GDP. The establishment of China's unified carbon market should be coordinated with regional development. Some supporting measures for regional ecological compensation and the mitigation of regional development are yet to be adopted.

Energy consumption structural adjustment and carbon neutrality in the post-COVID-19 era.

Since the spread of COVID-19 pandemic all over the world, a significant recession has broken out with no precedent. China has brought up a new voluntary contribution target that achieving carbon neutrality by 2060. How to achieve climate change mitigation targets without heavily hindering economic development is of great importance in the future. In this study, a Markov chain model is employed to forecast primary energy consumption (PEC) structure and verify whether the carbon intensity target would be achieved under three scenarios with different economic growth rates, such as 6.1%, 4.2%, and 2.3%, respectively. A multi-sector dynamic stochastic general equilibrium (DSGE) model is employed to simulate and evaluate economic development, fossil and non-fossil energy consumption, and CO2 emissions under three scenarios using data calibration according to the Markov chain prediction result. The prediction results from the Markov chain show that energy structural adjustment can help us achieve the carbon intensity target of 2030 under both steady and mid-speed development scenarios. As long as the economic growth rate is higher than 4.2%, the carbon intensity target can be achieved mainly through energy consumption structural change, which provides a way to achieve the carbon neutrality target of 2060. The simulation results from the DSGE model show that energy structural adjustment can also smooth the volatility of the economic fluctuation when exogenous stochastic shocks happened.

Pricing, Carbon Emission Reduction, Low-Carbon Promotion and Returning Decision in a Closed-Loop Supply Chain under Vertical and Horizontal Cooperation.

In this paper, we examine the influences of vertical and horizontal cooperation models on the optimal decisions and performance of a low-carbon closed-loop supply chain (CLSC) with a manufacturer and two retailers, and study optimal operation in the competitive pricing, competitive the low-carbon promotion, the carbon emission reduction, the used-products collection and the profits. We consider the completely decentralized model, M-R vertical cooperation model, R-R horizontal cooperation model, M-R-R vertical and horizontal cooperation model and completely centralized model, and also identify the optimal decision results and profits. It can be observed from a systematic comparison and numerical analysis that the completely centralized model is best in all optimal decision results among all models. In semi-cooperation, the M-R vertical cooperation model is positive, the R-R horizontal cooperation model is passive, and the positivity of the M-R-R vertical and horizontal cooperation model decreases with competitive intensity increasing in the used-products returning, carbon emissions reduction level, low-carbon promotion effort and the profits of the manufacturer and the entire supply chain.