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Food consumption has resulted in great carbon emissions in recent years. China has been facing the new mitigation challenge of carbon emissions with its emerging middle-income groups, especially in urban areas. This paper analyzes and predicts carbon emissions from food consumption (CEFC) of middle-income groups in the Yangtze River Economic Belt (YREB), which has a higher emerging middle-income group and the highest economic corridor in China. Few studies address CEFC analysis from middle-income groups perspective; thus, this paper calculates urban–rural CEFC of middle-income groups spanning 2010–2018 and predicts them in 2025, 2030 and 2035 using the extended IPAT model. Results are as follows: (1) YREB's urban–rural CEFC of middle-income groups show an upward trend and present a spatial pattern of "low–high–low–high" from the western region to the eastern region. Urban CEFC of middle-income groups are significantly higher than that of rural areas; (2) rural CEFC of middle-income groups in the YREB will first increase in 2025 and then decrease in 2030 and 2035. The growing trend of urban CEFC of middle-income groups will show a significant downward from 2025 to 2035. The growing proportion of urban CEFC of middle-income groups will exhibit an objective growth in 2035. Understanding the changing trend of CEFC of middle-income groups provides governments with differentiated and forward-looking suggestions toward coordinating middle-income groups' expansion and carbon emissions reduction. [ABSTRACT FROM AUTHOR]

The accountability audit of natural resources (AANR) is a major institutional arrangement for advancing the construction of an ecological civilization in China. Based on the panel data of 271 cities in China from 2005 to 2017, this paper investigates the relationship between the AANR and carbon dioxide (CO2) emissions using a multi-period difference-in-differences (DID) model. The results show that AANR significantly increases the CO2 emission reduction rate by 0.009 units at the 5% significance level. The results still hold after a series of robustness tests. Given all else being equal, this significant effect is 0.001. Further analyses show that AANR improves pilot cities' CO2 emission reduction rates mainly by enhancing their green innovation capability. The mediating effect of cities' green technology innovation capability accounts for 96.00%, while the AANR's direct effect only accounts for 4.00%. The AANR has significantly positive effects of 0.017% and 0.029% for western cities and cities with high fiscal pressure at the 5% and 1% significance levels, respectively. Therefore, strengthening AANR implementation by enhancing the mediating efficiency of cities' green technology innovations and implementing dynamically differentiated AANR policies in Chinese meso-cities will contribute to the achievement of China's carbon peaking and carbon neutrality targets. [ABSTRACT FROM AUTHOR]

Reducing greenhouse gas emissions is a crucial challenge in urban areas characterized by high energy consumption and reduced exposure to nature. In this context, the urban green system could play a pivotal role. In the literature, scholars have analyzed both the ability of species-specific and layout-specific green infrastructure to increase carbon sequestration and the best location sites for new green infrastructure to increase the provision of overall ecosystem services. There is a lack of studies helping green urban planners and designers choose where and which green infrastructure to implement based on vegetation species-specific performance and the local carbon emissions of city components. This paper uses tree inventory data from a medium-sized city in central Italy (Perugia) to develop a spatial analysis of urban park performance in carbon sequestration. Then, the method evaluates the carbon emission of a public city building to generate a spatialized balance between building demand and tree supply to support local decisions about the best locations for new green infrastructure and the choice between species. The paper contributes to GIS-based tools that vary the recommended location sites and species for new green infrastructure based on the demanded ecosystem service. [ABSTRACT FROM AUTHOR]

Under the goal of " carbon peaking and carbon neutrality," how to create a low-carbon and green development path for cities is an urgent problem in China. One of the effective ways to solve this problem is through industrial collaborative agglomeration. In this paper, panel data of 276 prefecture-level cities in China from 2010 to 2020 was selected to investigate the relationship between the collaborative agglomeration of manufacturing and producer service and carbon emission intensity by using a fixed-effects model, intermediary model, and SDM in multiple dimensions. The study found that the collaborative agglomeration of manufacturing and producer service industries significantly influences carbon emission intensity with an inverted U-shaped curve, and the agglomeration of manufacturing and producer service industries can influence carbon emission intensity through technological progress. In addition, the inverted U-shaped impact of collaborative agglomeration of manufacturing and producer services on carbon emission intensity has a spatial spillover effect, and the spatial spillover effect generated is stronger than the impact on the local area. Moreover, from the perspective of industry heterogeneity, compared with low-end producer services, the agglomeration of manufacturing and high-end producer services can play a better role in carbon emission reduction. Regarding regional heterogeneity, compared with the central and western regions, the impact of manufacturing and production services agglomeration on carbon intensity is far more obvious in the more economically developed eastern regions. [ABSTRACT FROM AUTHOR]

Since 2010, the Chinese government has developed several batches of low-carbon pilot provinces and cities continuously. And in the backdrop of carbon peak and carbon neutralisation recently, the carbon emission reduction policies and achievements of China have attracted more and more worldwide attention. In the field of human health, this paper investigates whether the Low-carbon Province Pilot Policy of China has positive effects on residents' health based on the micro data of China Family Panel Studies (CFPS) from 2010 to 2016. Using the time-varying difference-in-difference model to solve the endogeneity problem commonly faced in the literature, we find that Low-carbon Province Pilot Policy has a positive impact on public health through air quality improvement and the unemployment level alleviation mainly. And in urban areas, the policy effect is more obvious than that in rural regions. This paper expands the content of national low-carbon governance effects to residents' health further and contribute to China's low-carbon development. Specifically, it reminds us to focus on the dynamic adjustment and effective continuation of the policy, and also the all-round support for 'the disadvantaged' in environmental governance process. [ABSTRACT FROM AUTHOR]

How the transformation of trade patterns affects carbon emissions is an important topic in the field of trade and the environment. Based on the panel data of Chinese cities from 2000 to 2016, this paper uses fixed effect model, instrumental variable model and threshold estimation model to investigate the impact of the transformation of export trade patterns on carbon emissions and its internal mechanism. We find that during the investigation period, the transformation of export trade pattern has a significant "U"-shaped nonlinear impact on carbon emissions, and the research results still hold after a series of robustness checks and instrumental variable estimation; through the mechanism analysis of the threshold effect, it is found that in the process of the impact of trade patterns transformation on carbon emissions, there are double thresholds for energy structure transition; the nonlinear impact of export trade pattern transformation on carbon emissions has significant heterogeneity in different regions, city categories, and different periods; the mechanism analysis results show that industrial structure upgrading, industrial co-agglomeration, and green technology innovation are the main paths that the transformation of export trade patterns affects carbon emissions. Under the dual goals of fulfilling China's commitment to "carbon peak in 2030 and carbon neutrality in 2060" and implementing corporate social governance to promote green and sustainable development, this paper will provide suggestions for realizing the goal of low-carbon emission reduction from the perspective of the transformation of export trade patterns. [ABSTRACT FROM AUTHOR]

Improving total factor carbon emission efficiency (TCE) is the key to achieving carbon emission reduction targets while ensuring economic growth. In this paper, the global Malmquist index based on the SBM model is used to measure TCE of 283 cities in China from 2011 to 2019. On this basis, this paper uses the spatial econometric model and intermediary effect model to empirically analyze the impact of industrial co-agglomeration on TCE and its transmission mechanism. Furthermore, considering the differences in geographical location and resource endowment among regions, this paper analyzes the heterogeneous effect of industrial collaboration agglomeration on TCE in different regions and cities. The results show that: (1) Industrial co-agglomeration can improve TCE, and its main transmission channel is technological innovation. (2) Industrial co-agglomeration has a positive spatial spillover effect. Industrial co-agglomeration in one region can improve the TCE in the surrounding regions. (3) Industrial co-agglomeration of cities with different geographic locations and resource endowments has a heterogeneous effect on TCE. Regarding geographical heterogeneity, the industrial co-agglomeration in the eastern region has the greatest promoting effect on TCE, followed by the central region. However, the impact of industrial co-agglomeration in the western region on TCE is not significant. Regarding resource endowment heterogeneity, the industrial co-agglomeration in non-resource-based cities has a greater promoting effect on TCE than that in resource-based cities. [ABSTRACT FROM AUTHOR]

As the digital economy grows rapidly around the world, how would it impact carbon emissions? This paper discusses this issue from the perspective of heterogeneous innovation. Based on the panel data of 284 cities in China from 2011 to 2020, this paper empirically examines the impact of the digital economy on carbon emissions as well as the mediating and threshold effects of different innovation modes. The study finds that the digital economy can significantly reduce carbon emissions; this conclusion holds after a series of robustness tests. Independent innovation and imitative innovation are important channels through which the digital economy affects carbon emissions, but technological introduction is not an effective way. For regions with high levels of financial investment in science and innovative talents, the reduction in carbon emissions from the digital economy is more significant. Further research shows that the effect of the digital economy on carbon emissions has a threshold feature, with an inverted U-shaped relationship between the two, and that an increase in autonomous innovation and imitation innovation can enhance the digital economy's carbon reduction effect. Therefore, it is necessary to strengthen the capacity of independent innovation and imitation innovation to exploit the carbon-cutting effect of the digital economy. [ABSTRACT FROM AUTHOR]

As urbanization continues to accelerate worldwide, the consequential rise in CO2 emissions has caused substantial environmental challenges. Urban regeneration has emerged as a promising approach to reducing carbon emissions and developing low-carbon cities. Even though both urban regeneration and carbon emissions reduction have been researched from various perspectives, a thorough review is still required to completely reveal their multifaceted relationship. Based on 231 papers published between 2001 and 2023, a bibliometric analysis was conducted to understand the overall trajectory and main focus of the existing research. Then, we qualitatively analyzed the main findings from bibliometric results in terms of key regeneration elements, specific regeneration strategies, research methodologies, as well as research trends and agendas. The results indicated that research in this field is gradually becoming more specialized and comprehensive. Buildings and energy have always been two key urban regeneration elements and research hotspots. Additionally, as a systematic project, reducing carbon emissions requires further exploration of other regeneration elements' contributions and their interactions in the urban system, which needs the corresponding support of more specific regeneration strategies and research methodologies. These findings can advance the development of innovative and impactful pathways for low-carbon oriented urban regeneration, leading ultimately to sustainable cities. [ABSTRACT FROM AUTHOR]

Urban carbon emissions are an important area for addressing climate change, and it is necessary to establish scientific and effective carbon emission prediction models to formulate reasonable emission reduction policies and measures. In this paper, a novel model based on Lasso regression, an ARIMA model, and a BPNN is proposed. Lasso regression is used to screen the key factors affecting carbon emissions, and the ARIMA model is used to extract the linear components of the carbon emission sequences, while the BPNN is used to predict the residuals of the ARIMA model. The final result is the sum of that from the ARIMA model and the BPNN. The carbon peak, carbon neutralization time, and emissions were analyzed under different scenarios. Taking Suzhou City as an example, the results show that the electricity consumption of the whole population is one of the key drivers of carbon emissions; the carbon emission prediction accuracy and stability of the ARIMA-BPNN combined model are better than those of the single model, which improves the reliability as well as the accuracy of the model's prediction. However, under the constraints of the current policies, the goal of achieving carbon peaking by 2030 in Suzhou City may not be realized as scheduled. This novel carbon emission prediction model built was validated to provide a scientific basis for low-carbon urban development. This study presents an important reference value for predicting carbon emissions and formulating emission reduction measures in other cities. [ABSTRACT FROM AUTHOR]

China, as the world's largest carbon dioxide emitter, is bound to assume the important responsibility of energy conservation and emission reduction. To this end, each city, led by representative municipalities, must enhance efforts in carbon emission reduction to jointly realize China's low-carbon transition. Taking four representative municipalities, namely, Beijing, Tianjin, Shanghai, and Chongqing as the case cities, this paper establishes a decomposition analysis for the driving factors of carbon emissions by applying the LMDI method covering data from 2007 to 2017. Kaya identity is used to decompose the effects into eight driving factors: GDP effect, industrial structure effect, energy intensity effect, overall energy structure effect, population effect, urbanization effect, per capita energy consumption effect, urban and rural energy structure effect. The results show that at the municipality level, the driving factors that contribute to carbon emissions are the GDP growth effect and the population effect, with the former still being the most important factor in the municipalities with faster economic growth; and industrial structure effect is the most important factor that inhibits carbon emissions, followed by energy structure effect. This paper considers the driving factors of both the production side and the residential consumption side from the city level. The research reveals the main driving factors that effect the carbon emissions of megacities in developing countries, and highlights the leading role of megacities in terms of carbon emission reduction in China and even the world. The paper thereby puts forward policy implications for China's economic policies. [ABSTRACT FROM AUTHOR]

It will contribute to ameliorating the national carbon trading system and further benefit the realization of China's carbon peaking and carbon neutrality goals through in-depth study and summarize the effectiveness of carbon trading policy implementation in pilot areas. Based on the multi-phase difference-in-differences approach and synthetic control method, this paper utilizes China's provincial panel data from 2006 to 2020 to comprehensively assess the Porter effect (including the economic and green innovation effects) and carbon mitigation effect of carbon trading policy from the overall and local perspectives. Then the robustness test and mechanism analysis are carried out. The findings show that: (1) from the overall effect, the carbon trading policy brings a noticeable carbon mitigation effect and Porter effect, and the intensity of the impact keeps changing over time; (2) there is significant heterogeneity in the local effects of this policy on the eight pilot provinces and cities, with only Chongqing obtaining both the Porter effect and the carbon mitigation effect, while the rest of the regions do not yield the triple dividend of economic, environmental, and green innovation simultaneously; (3) there are much more prominent environment and green innovation effects in the developed regions, while the underdeveloped ones have better economic development and no green innovation effect; (4) the policy can bring environmental and economic dividends through energy structure optimization. However, due to the carbon rebound effect, stimulating green innovation pathways can only boost the economy while carbon emissions increase instead. The research conclusions can provide the decision support and policy direction for policies related to China's carbon market. [ABSTRACT FROM AUTHOR]

• We study the relationship between GVC embedding and carbon emission density. • A mediation effect model was established to test the mechanism. • There is a U-shaped relationship between urban GVC embedding and carbon emission density. To achieve its carbon peak and neutrality plan, whether China's global value chain (GVC) embedding has a carbon emission reduction effect is a question worthy of attention. This paper measures the GVC embedding degree of 218 Chinese cities from 2000 to 2016, and constructs a panel data model to investigate the impact and its mechanism of GVC embedding on carbon emission intensity. This study finds that there is a significant U-shaped relationship between GVC embedding and carbon emission intensity, and a series of robustness test results support this conclusion. Export scale, energy consumption structure and technological innovation play a mediation role in the impact path of GVC embedding on carbon emission intensity. Based on this, this paper proposes that while participating in GVC, China needs to actively utilize the emission reduction effect of GVC, promote the transformation of export mode to high quality, reduce the use and dependence on high-carbon resources such as coal, and encourage companies to innovate. [ABSTRACT FROM AUTHOR]

The variation in the urban spatial structure (USS) has profound impacts on carbon emissions. Studying the relationship between the two can provide guidance for carbon neutrality strategies and the construction of low-carbon cities in China. However, there is currently a lack of comparative research on the different regions within a province. In this paper, the spatiotemporal evolution of the USS and carbon emissions, at five-year intervals from 2000 to 2020, is investigated in 21 prefecture-level cities in Guangdong Province, China, and the overall relationship of the USS to carbon emissions and their spatiotemporal variations are analyzed by using a two-way fixed-effects model and a geographically and temporally weighted regression model, respectively. The results show that, first, over the past twenty years, the scale of cities has continued to expand, with increasing continuity and aggregation in the built-up areas, while the complexity and fragmentation of their shapes have gradually decreased. Second, the gap in carbon emissions between the Pearl River Delta and other regions in Guangdong shows a trend of first decreasing and then increasing, with high values concentrated in the Pearl River Delta region and the city of Shantou in the east. Third, compared to socio-economic factors, the USS has a more direct and pronounced impact on carbon emissions. Urban expansion and the increased complexity of land patches promote carbon emissions, whereas improving urban spatial continuity and compactness can reduce carbon emissions. Fourth, the dominant spatial structure indicators of carbon emissions differ among the regions of eastern, western, and northern Guangdong and the Pearl River Delta. This study proposes spatial optimization strategies for the low-carbon development of cities in Guangdong Province, providing a new perspective for integrating urban layout and emission reduction policies. [ABSTRACT FROM AUTHOR]

Industrial land is currently the main carrier and important source of global carbon emissions, and as the world's largest developing country, China's large-scale and diversified industrial land supply has made it the world's largest carbon emitter. Therefore, researching the impact of different supply methods of industrial land on carbon emissions and its impact paths in China can help provide a reference for other countries to reduce carbon emissions from the perspective of urban industrial land management, which is of great significance for effectively promoting global carbon reduction. Based on this, this paper analyses the impact of different supply methods of industrial land on carbon emissions and its urban heterogeneity using the SYS-GMM and chain-mediated effects models for 285 cities in China from 2008 to 2020. The study found that, in general, the impact of different industrial land transfer modes on carbon emission has hysteresis and persistence. Agreement and listing transfer with government intervention can significantly exacerbate carbon emissions, while more market-based bidding and auction transfer can dampen carbon emissions. In terms of intermediary effects, the transfer of industrial land by agreement and listing will inhibit the rationalization and advancement of industrial structure, thus aggravating carbon emissions. The transfer of industrial land by bidding and auction will create barriers to entry and a crowding-out effect, promote the rationalization of industrial structure and the transformation and upgrading of industrial structure and moderate carbon emissions. In terms of city heterogeneity, there is urban heterogeneity in the impact of industrial land transfer on carbon emissions in cities with different economic types. Bidding and auction transfer for industrial land in both economically developed and less developed cities can promote carbon pollution. While the more developed urban economy makes the intermediary effect of industrial structure not significant. In the future, it is necessary to strictly control the scale of industrial land supply; the whole process supervision mechanism of industrial land allocation and differentiated industrial land supply strategies will provide useful experience for many developing countries in allocating industrial land to mitigate carbon emissions, generating effective contributions to global carbon emission reduction. [ABSTRACT FROM AUTHOR]

An accurate evaluation of carbon emission efficiency (CEE) at the city level can provide guidelines for understanding low carbon performance, which is crucial to achieving dual carbon targets. Existing CEE studies focused on national, industrial, and provincial scales while neglecting the city level and failing to consider competing relationships among decision-making units in their measurement models. To fill these gaps, this paper introduces the data envelopment analysis game cross-efficiency model (DEA-GCE) to measure urban CEE performance and compares it with the traditional Super-SBM model using the data from 283 Chinese cities between 2006 and 2019. The results show that (1) the DEA-GCE method provided more intensive and stable results. (2) Overall CEE of Chinese cities declined slightly amidst fluctuations during this period. (3) CEE in cities exhibits spatial clustering characteristics. CEE performance in Northeast China has improved, while CEE in Northwest China continues to lag behind. This study introduced an innovative method for calculating urban CEE and conducted an empirical study of 283 Chinese cities, which has implications for formulation of emission reduction policies. [ABSTRACT FROM AUTHOR]

Considering the opening of high-speed railway (HSR) as a quasi-natural experiment, this paper explores the emission reduction effect of HSR opening and its mechanisms and heterogeneity characteristics based on the panel data of 272 cities in China from 2006 to 2019 by staggered DID model and the mediating effect model. It is found that (1) the opening of HSR significantly reduces the carbon emission level of cities, and the conclusion still holds after controlling the endogeneity issue and a series of robustness tests; (2) Bancon decomposition and negative weight diagnostics both show that the estimated average treatment effect bias is small and the robust estimators are more robust; (3) by reducing production costs and promoting inter-regional industrial division of labor, the opening of HSR promotes intra-industrial structural transformation and inter-industrial structural transformation, respectively, thus reducing urban carbon emissions. At the same time, HSR achieves urban carbon emission reduction by promoting free movement of population and human capital agglomeration; and (4) further study finds that there is regional heterogeneity in the emission reduction effect of HSR opening, and the carbon reduction effect is mainly reflected in large and medium-sized cities, near provincial capitals, non-resource-based cities, and cities with rich educational resources, which indicates that the location of future HSR should be combined with the city's own characteristics and be tailored to the city. [ABSTRACT FROM AUTHOR]

The formulation of long-term step-by-step emission reduction plan is an important step for effective scientific emission reduction. This paper takes Shanghai as the research object, constructs PSO-LSTM model on the basis of STIRPAT model, and further constructs three dynamic policy scenarios combined with China's actual situation and makes short-, medium-, and long-term multivariate predictions for them. The study finds that only the improvement of energy consumption structure has a promotion effect on carbon emission reduction, and urbanization, industrial structure, technology level, population, and economic level all have an increasing effect, and secondly, the carbon emission reduction path of Shanghai basically achieves the core objective of steady decrease under the three scenarios. Secondly, under the three scenarios, Shanghai's carbon emission reduction path basically achieves the core objective of steady decline, but the decline in the GU scenario is more significant. It is recommended that Shanghai further adjusts its industrial structure, optimizes its energy consumption structure, promotes technological innovation and progress, and promotes the development of the circular economy model. [ABSTRACT FROM AUTHOR]

The low-carbon transition is a systemic economic and social change that will inevitably have an impact on many areas of the urban system. Has China's ongoing low-carbon transition impacted urban resilience (UR) systems while achieving urban energy saving and carbon emission reduction goals? This paper uses the implementation of the carbon emissions trading pilot policy (CETPP) as a "quasi-natural experiment." It evaluates the impact of the policy on UR using a difference-in-differences model based on the data of prefecture-level cities from 2008 to 2020. The study shows that pilot carbon trading policies favor UR, and the market mechanism of carbon emissions has a heterogeneous cause influence on UR. The impact of pilot carbon trading policies on UR varies according to the respective moderating effects of institutional factors, green technology innovation, industrial structure rationalization, and output effects. [ABSTRACT FROM AUTHOR]

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Road transport has been one of the largest contributors to global carbon emissions. In the Philippines, one such measure is the introduction of a relatively new public transport mode called the “Premium Point-to-Point (P2P) Bus Service”, which features shorter travel time and lower emission intensity than conventional buses. In light of the significant deficiency in transport emission studies for Philippine cities, this paper presents a scenario-based method for quantifying the potential emission reduction benefits of the P2P bus in Metropolitan Manila. Based on a pro rata mode shift assumption, it is estimated that a 1% mode shift of passengers from existing motor modes to P2P buses would lead to 0.64% reduction of total emissions over selected travel routes in Metro Manila. The analytical method addresses the acute emission data scarcity in the Philippines, and provides early evidence on the aggregate environmental benefits of P2P buses. Extensive sensitivity tests are conducted to verify the simulation results and identify key determinants of emission reduction. The tests confirm the robustness of research findings and further uncover the great potential of increasing vehicle occupancy levels to mitigate vehicle emissions in Metro Manila. Policy implications for enhancing the environmental benefits of P2P buses are provided. [ABSTRACT FROM AUTHOR]

The establishment of green finance reform and innovation pilot zones is an important practical exploration to achieve carbon emission reduction goals through green finance in China. Based on the panel data of 282 Chinese prefecture-level cities from 2006 to 2019, this paper examines the mechanism of China's green financial reform and innovation pilot zone policy (GFRI) on urban carbon emissions (CE) and carbon emission efficiency(CEE) using difference-in-differences model. The study shows that GFRI has a significant carbon emission reduction effect, which is reflected in the significant reduction of urban CE and the improvement of urban CEE. GFRI achieves carbon emission reduction by promoting urban green innovation, while the mediating effect of financial agglomeration has not been verified. The results of heterogeneity analysis show that GFRI has more significant effects on carbon emission reduction in non-resource-based cities, large-scale cities and cities with strict environmental regulation. Financial development and digital infrastructure play a positive moderating role on the carbon emission reduction effect of GFRI. This study provides empirical evidence and policy insights from the Chinese city level for deepening the green finance policy and promoting urban low-carbon development. [ABSTRACT FROM AUTHOR]

The construction of high-speed railways and the smart city pilot (HSR-SCP) demonstrates the synergy of traditional and new infrastructure upgrades, which has a significant influence on the economic transition and green economic growth. However, little study has been conducted on the impact of HSR-SCP synergy on carbon emissions. Applying China's prefecture-level panel data, this paper, therefore, attempts to investigate the effect of HSR-SCP on carbon emissions and uncover its in-depth mechanism through the multi-period difference-in-differences model. Notably, an appropriate instrumental variable for HSR-SCP is introduced to address possible endogeneity. The main conclusions indicate that the construction of HSR-SCP plays an essential role in cutting carbon emissions, which is still valid after adopting the instrumental variable method and some robustness tests. Furthermore, green technology innovation plays a crucial intermediary role in that relationship. Finally, the carbon reduction effect of HSR-SCP is significant in Mid-Eastern China and cities with high population sizes and administrative levels, but not in other cities or regions. The research conclusions demonstrate the significant synergy of traditional and new infrastructure upgrades on carbon reduction and provide an empirical foundation for developing countries to realize low-carbon development through optimizing smart infrastructure and promoting green technology innovation. [ABSTRACT FROM AUTHOR]

The map of radiative cooling potential shows that the radiative cooling power can reach up to 100 W/m2 for its application in different climatic zones. However, the effect of building deployment on the city-scale application of radiative cooling is generally ignored in majority of existing studies, which overestimates the cooling benefit. This paper investigates the cooling benefit of implementing radiative cooling on a city-scale by considering building deployment. To determine the effect of building deployment on the radiative cooling potential, the city of Xi'an in China is modeled using COMSOL and verified with real weather data. Then, the thermal response of buildings with radiative cooling envelopes is discussed in detail. Finally, carbon emission reduction is derived at the city-scale by applying radiative cooling considering building deployment. The results show that the estimated radiative cooling power is approximately decreased by 14.7% for the ideal broadband surface and 47.1% for the ideal selective surface on the selected day by considering building deployment on a city-scale. Furthermore, carbon emission reductions of 0.52 gCO 2 /(m2⋅h) and 0.16 gCO 2 /(m2⋅h) can be achieved from the roof and walls on the selected day by applying radiative cooling material on both the roof and walls. This study can provide guiding significance for the large-scale application of radiative cooling in cities. [ABSTRACT FROM AUTHOR]

High carbon emissions and population aging are two obstacles to China's development as an emerging economy. It is urgent to scientifically examine the impacts of population aging on carbon emissions to discover new pathways for urban carbon emission reduction. However, existing studies face challenges in terms of focusing on and methodology for accurately capturing the role of industrial production and energy consumption in the environmental effects of population aging. This paper constructs a chain mediation analytical framework and systematically examines the intrinsic linkages between population aging and carbon emissions in Chinese cities from 2000 to 2020 using a two-way fixed effects model. This paper has three main findings. (1) The positive correlation between urban population aging and carbon emissions development is generally low, with coupling coordination degree values of 0.4233, 0.4458, 0.4220, 0.4715, and 0.4665 for each yearly cross-section. (2) For every 1% increase in the population aging rate, carbon emissions decrease by 0.3478% on average. The carbon reduction effect of population aging is significantly greater in low-emissions cities and high-income cities than in other cities. (3) The industrial upgrading (IU) path, energy conservation (EC) path and chain mediation (CM) path account for 72.43%, 7.23% and 20.34%, respectively, of the indirect effects. If the causal link between IU and EC is not considered, the EC effect will be overestimated by 281.16%. The results of the study suggest that properly coping with population aging and reducing carbon emissions are not completely opposed to each other, a conclusion that passes both endogeneity exclusion and robustness check. This paper advocates replacing the one-size-fits-all approach in carbon emissions management and investing more in policy preferences to support carbon emission reduction in both high-emissions and low-income cities. [Display omitted] •The synergistic trends of carbon emissions and population aging are not apparent. •Population aging significantly reduces carbon emissions at the city scale. •Linkage effect of industrial upgrading and energy conservation on carbon emission reduction. •Energy conservation effect of population aging amplified by industrial upgrading. [ABSTRACT FROM AUTHOR]

Addressing global warming is one of the most pressing environmental challenges and a crucial agenda for humanity. In this literature study, we employed bibliometrics to reproduce nearly two decades of research on carbon emission reduction in China, the largest carbon emitter worldwide. The scientometrics analysis was conducted on 1570 academic works published between 2001 and 2021 concerning China's carbon emission reduction to characterize the knowledge landscape. Using CiteSpace and VOSviewer, the basic characteristics, research forces, knowledge base, research topic evolution, and research hotspots were identified and revealed. The analysis results show that the attention to and research on China's carbon emissions have increased in recent years, giving rise to leading institutions and relatively stable core journal groups in this field. The research disciplines are relatively concentrated, but the research collaboration needs strengthening. The research hotspots are mainly carbon emission causes, impacts, and countermeasures in China, and the research frontiers have been constantly advanced and expanded. In the future, research on countermeasures needs more effort, and research cooperation needs to strengthen. The changing landscape of hotspot clusters reveals China's transition towards a low-carbon economy. Through comprehensive analysis of the potential and obstacles to China's transition to low-carbon development, we identified three promising areas of action (low-carbon cities, low-carbon technologies and industries, and transforming China's energy system) and proposed research directions to address remaining gaps systematically. [ABSTRACT FROM AUTHOR]

Institutions are the fundamental determinants of carbon emission performance. However, the environmental impact of intellectual property institution, especially its impact on carbon emissions, has been paid little attention. Therefore, the main purpose of this study is to assess the effect of intellectual property institution on carbon emission reduction, revealing a new solution to control carbon emissions. To achieve the goal, this study regards the National Intellectual Property Demonstration City (NIPDC) policy in China as a quasi-natural experiment of intellectual property institution construction and exploits the difference in difference approach to objectively evaluate the impact of intellectual property institution on carbon emission reduction based on the panel data of China's cities. The study draws the following important conclusions. First, compared with non-pilot cities, the NIPDC policy has reduced urban carbon emissions by 8.64% in pilot cities. In particular, the "carbon emission reduction dividend" of the NIPDC policy is in the long term but not in the short term. Second, the influence mechanism analysis shows that the NIPDC policy can promote carbon emission reduction by stimulating technology innovation, especially breakthrough innovation. Third, the space overflow analysis reveals that the NIPDC policy can mitigate carbon emissions in adjacent areas, resulting in obvious spatial radiation effect. Fourth, the heterogeneity analysis confirms that the carbon emission reduction effect of the NIPDC policy is more obvious in low administrative hierarchic cities, small and medium-sized cities, and western cities. As a result, Chinese policymakers should orderly promote the construction of NIPDCs, strengthen technology innovation, give full play to the spatial radiation role of NIPDCs, and optimize the role of government, so as to better release the carbon emission abatement effect of intellectual property institution. [ABSTRACT FROM AUTHOR]

In this paper, 41 prefecture level cities in the Yangtze River Delta (YRD) are selected as the research objects. Based on the semantic mining model, the policy data information from three dimensions namely national, provincial and municipal levels is integrated to build data-driven multiple scenarios. Then the long short-term memory neural network optimized by particle swarm optimization (PSO-LSTM) is further used to predict the peaking time of the YRD urban agglomeration under multiple scenarios. The results show that the YRD urban agglomeration as a whole can achieve carbon peaking on time under all three simulation paths, but the peaking situation of specific cities within the region will be significantly different due to differentiated policy orientations. Under the business as usual scenario, some cities in the north of the region cannot achieve carbon peaking by 2030. Under the stable growth scenario, more cities cannot achieve carbon peaking on time. However, under the low carbon scenario, all cities within the region can achieve carbon peaking by 2030. Therefore, when exploring effective carbon peaking paths, adopting more stringent policies and measures to limit carbon emissions can help achieve the regional carbon peaking goal earlier, ensure that all cities within the region can achieve carbon peaking on time, and can also narrow the gap between cities within the region in terms of peaking time. This paper explores a new scenario analysis parameter setting and carbon emission prediction method, which overcomes the subjectivity of current scenario analysis research in parameter setting to a certain extent, and also provides a scientific reference for predicting the diversified development trend of carbon emission. • The semantic mining method is used to determine the scenario parameters. • PSO-LSTM is used to improve the accuracy of carbon emission prediction. • Multiple scenarios of carbon emission development trends. • Differentiated emission reduction paths for cities. [ABSTRACT FROM AUTHOR]

• The spatiotemporal interactions of carbon emission efficiency(CEE) were explored. • A MGWR model was used to estimate the scale effects of factors influencing CEE. • The evolution of the spatial pattern of CEE had strong spatial integration. • CEE agglomeration changes had path dependence and spatial locking. • The factors influencing CEE exhibit obvious scale heterogeneity. Exploring the spatiotemporal evolution and influencing factors of carbon emission efficiency (CEE) is crucial for achieving the goal of urban carbon neutrality. However, most of the existing studies ignore the temporal dependence of the spatial pattern evolution of CEE and the scale variability of the factors influencing CEE. With the help of an exploratory spatiotemporal data analysis framework, this paper examined the spatiotemporal interactions of CEE across 110 cities in the Yangtze River Economic Belt (YREB). In addition, a multiscale geographically weighted regression model was employed to reveal the scale effects of influencing factors on CEE. The main conclusions are as follows: first, the CEE of cities in the YREB shows a fluctuating upward trend, but the overall level is still low, and a certain polarization phenomenon exists. Second, the spatial pattern of the CEE of cities in the YREB is generally relatively stable, with strong spatial integration and path dependence. Finally, the factors influencing CEE exhibit obvious scale variability and spatial heterogeneity. Our findings can provide a basis for localized and differentiated carbon emission reduction decision-making at the city level, as well as new insights for the formulation of sustainable urban spatial planning and low-carbon development strategies. [ABSTRACT FROM AUTHOR]

Forecasting the future emission trajectories and the relating sensitive driving factors of emissions for cities is of great significance to formulate realizable CO 2 mitigation policies. To proceed the forecasting, studies on peak prediction and quantification of reduction potential at the city level are essential. However, the studies in the area are very limited. Selecting the Beijing-Tianjin-Hebei urban agglomeration (BTH) as the study region, this paper aims to contribute to the research area and provides implications for other cities or urban agglomerations. The Kaya identity and multi-scenario simulation were employed to predict the dynamic evolution pathways of CO 2 emissions from 2021 to 2035 and explore the differential CO 2 peak time, peak value, and reduction potential for 13 cities in BTH. Monte Carlo simulation, Mann-Kendall trend test and Sen's slope estimation method are jointly used to reduce uncertainties in estimation. The Monte Carlo simulation results show that most cities in BTH have already reached their CO 2 emissions peak, while Tianjin, Langfang, Cangzhou and Tangshan are expected to reach their peaks between 2025 and 2030. Among them, 5 and 8 cities have the risk of not reaching their peak before 2035 in the high consumption scenario (HCS) and extensive development scenario (EDS) respectively. Comparative analysis reveals that low-carbon scenario (LCS) and sustainable development scenario (SDS) have significant effects on emissions reductions. The top three cities in terms of accumulative emission reduction in 2021–2035 are Tianjin, Tangshan and Cangzhou, estimated as 117.82–250.75 Mt CO 2 in LCS and 217.77–454.10 Mt CO 2 in SDS, respectively. The results of sensitivity analysis reveal that the most critical driver of CO 2 emissions in Beijing is population, while that is GDP per capita for Tianjin. Langfang and Hengshui showed the highest sensitivity to energy intensity. Accordingly, these cities have differentiated concerns and priorities to achieve their carbon peak goal as scheduled. • Kaya identity and scenario simulation are combined to predict future CO 2 emissions. • The BTH would reach CO 2 emission peak by 2025, but the time varies on city-level. • Tangshan, Langfang, and Cangzhou are at greater risk of not reaching the peak by 2030. • Tianjin, Tangshan and Cangzhou have the greatest potential to reduce emissions. • Differentiated priorities are suggested for specific city's CO 2 mitigation targets. [ABSTRACT FROM AUTHOR]

Green and low carbon transition is a broad and profound economic and social systematic change. Green innovation is a critical way to promote energy saving and emission reduction. Has China continuously promoted a carbon emission trading policy to significantly promote green innovation cooperation? Taking the implementation of the carbon emission trading pilot policy as a "quasi-natural experiment," this study answers this question by exploring the impact of the policy on green innovation cooperation. Based on data on 274 cities from 2008 to 2020, the multi-time difference-in-differences model is used to evaluate the impact of the policy on green innovation cooperation. The results reveal that the carbon emission trading pilot policy significantly improved inter- and intra-city green innovation cooperation through the upgrading effect of industrial structure and the coverage effect of digital finance compared with the non-pilot cities at the city level. In addition, there are significant differences in the policy effects among cities with different degrees of openness to the outside world and command-and-control environmental regulation. [ABSTRACT FROM AUTHOR]

Exploring the relationship between economic development and carbon emissions is a hot issue of concern to academia. Taking Chinese cities as the research object, this study constructed an allometric growth model of economic scale and carbon emissions to analyze the spatial–temporal evolution of their allometric growth from 2000 to 2017. Additionally, the geographical detector model was used to reveal the driving mechanism of allometric growth. The findings were as follows. (1) The spatial–temporal patterns of economic scale and carbon emissions were dominated by hot spots. Additionally, their size distribution was in an equilibrium stage. (2) The gap between the economic growth rate of Chinese cities and the growth rate of carbon emissions grew, reflecting the remarkable achievements in carbon emission reduction in Chinese cities as a whole. The scaling exponent declined from east to the west during 2000–2008, but showed the opposite trend during 2009–2017. The proportion of cities with negative allometric growth in the sample cities increased from 76.49 to 97.86%. (3) The influence of city investment intensity, energy utilization efficiency, technological development level, social consumption level, fiscal investment level and economic development level on allometric growth gradually decreases. These factors were also the main factors affecting the spatial–temporal heterogeneity of allometric growth. (4) The impact of various factors had a synergetic enhancement effect. These factors can be classified as economic factors, environmental factors and a combination of them. Under the nonlinear coupling of multiple factors, a spatially differentiated pattern of allometric growth was formed. [ABSTRACT FROM AUTHOR]

Reducing carbon emissions is of paramount importance to the accomplishment of the 2030 Sustainable Development Goals. The effect of rural e-commerce on agricultural carbon emissions (ACEs) is controversial, and particularly the mechanism behind the effect is unknown. To identify the impact of rural e-commerce on agricultural carbon emissions and its mechanisms, we take advantage of China's Rural E-Commerce Demonstration County Program (REDCP) as a quasi-natural experiment and use the multi-period difference-in-difference (DID) model to investigate the relationship between rural e-commerce and agricultural carbon emissions. Our data are based on panel data of 355 prefecture-level cities from 2010 to 2019 in China. We identify that rural e-commerce can reduce agricultural carbon emissions by an average of 14.4%, but this effect is not long-lasting. Mechanism analyses suggest that the reduction effect of rural e-commerce on agricultural carbon emissions is mainly due to fostering agricultural economic growth, increasing the share of low-carbon industry, and improving agricultural total factor productivity (TFP). Further heterogeneity analyses demonstrate that rural e-commerce has better carbon emissions reduction performance in eastern cities as well as in non-major grain-producing cities in China. [ABSTRACT FROM AUTHOR]

This study aims to develop a framework linking New-Type Urbanization (NTU) and carbon emissions, considering both direct and spillover effects. Firstly, the paper constructs a multi-dimensional index to measure NTU and documents the spatial patterns of NTU levels in China, showing remarkable inequality between the eastern and western regions. Secondly, the study evaluates the effectiveness of pilot policies aimed at achieving Smart City and NTU goals. Using the Difference-in-Difference model based on panel data collected from 108 cities in the Yangtze River Economic Belt from 2006 to 2019, the study provides empirical evidence that these policies can significantly reduce carbon emissions in terms of production and consumption. Moreover, the study finds that the policies have significant spatial spillover effects on neighboring cities based on the Spatial Panel Durbin Model. The findings suggest that such policies can effectively reduce carbon emissions in areas with significant human activity and have important policy implications for achieving sustainable development in China. • The research assesses how New-Type Urbanization affects emission reduction. • Policy pilots for Smart City & New-type Urbanization reduced environmental emissions. • The impact of emission reduction is spatially heterogeneous. • New-Type Urbanization can increase environmental emissions in nearby cities. [ABSTRACT FROM AUTHOR]

The power industry is a major source of carbon emissions in China. In order to better explore the driving factors of carbon emissions in China's power industry and assist the Chinese government in formulating emission reduction strategies for the power industry, this study applies the improved production-theoretical decomposition analysis (PDA) method to analyze the carbon emission drivers of China's power industry. This study investigates the impact of energy intensity, per capita GDP, population density, power generation structure, and environmental climate on carbon emissions in China's power industry in 30 provinces from 2005 to 2020. It was found that the carbon emission ratios of the power sector in all provinces and cities are basically greater than 1, which indicates that carbon emissions in most of the power sectors in the country are still increasing as of 2020. Overall, the effects of potential thermal fuel carbon emission efficiency, potential thermal energy consumption efficiency, the carbon emission efficiency of thermal power generation, economic scale, population density, and annual rainfall change are mostly greater than 1 and will promote the growth of carbon emissions in the power sector. Moreover, the effects of thermal power generation energy efficiency technology, thermal power generation emission reduction technology, power generation structure, and power generation per unit GDP are mostly less than 1 and will inhibit the growth of carbon emissions in the power sector. However, each of these drivers does not have the same degree of influence and impact effect for each province and city. Based on the research results, some policy recommendations are proposed. [ABSTRACT FROM AUTHOR]

Technological progress is considered an effective means to reduce carbon emissions, and an innovative city pilot scheme may contribute to this target. However, the impact of innovation city construction on carbon emissions has not been systematically studied. Based on panel data of 285 cities at prefecture level and above in China from 2003 to 2017, this study employs the difference-in-differences approach to evaluate the impact of innovative city pilots on CO2 emissions. The results show that CO2 emissions in pilot cities are 17.1% lower than that in non-pilot cities, indicating that the scheme contributes significantly to carbon emission reduction. Meanwhile, the spatiotemporal characteristics suggest that the local effect increases over time and the spillover effect is significant within 150–250 km. Additionally, a heterogeneity analysis shows that the impact of the pilot scheme on CO2 emissions is mainly reflected in the central region and prefecture-level cities. Lastly, mechanism analysis indicates that the pilot policy reduces CO2 emissions by promoting technological innovation, industrial upgrading, and environmental governance, of which technological innovation plays the most important mediating role. [ABSTRACT FROM AUTHOR]

As a gathering place for human production activities, cities are the main places where energy consumption and carbon dioxide emissions occur. How to accurately measure city size and test the impact mechanism of city size on carbon emissions of different city levels is still controversial. This study uses the global nighttime light data to identify urban bright areas and built-up areas, and accordingly constructs the city size index of 259 prefecture level cities in China from 2003 to 2019. It avoids the problem of only considering the single index of population size or space size, and makes the measurement of city size more reasonable. We use a dynamic panel model to study the impact of city size on urban carbon emissions per capita, and discuss the heterogeneity of various cities under different population levels and economic development levels. The empirical results indicate that in the scale of cities in China showed a fluctuating growth trend in recent years. The city size index of most cities is clustered at medium and high values. The city size index of cities with different economic development levels and different population-scale levels shows obvious gradient differences but maintains an upward trend. The expansion of supercities (with a population of more than 5 million) introduces a drastic increase in carbon emissions. The carbon emissions growth caused by the expansion of cities that are classified as third tier and below is the smallest, while that caused by the expansion of cities that are classified as first-tier is the largest. The findings suggest differentiated emissions reduction suggestions for cities with different sizes. [ABSTRACT FROM AUTHOR]

Forecasting cities' carbon emissions is an essential support for peak carbon emissions. Previous studies have mainly focused on estimating carbon emissions at large regional scales. Higher spatial resolution mapping of carbon emissions and simulation of future scenarios are important to support locally appropriate policy guidance to reduce carbon emissions. This paper proposes a bottom-up cadastral parcel-scale Carbon emission forecasting framework based on V ector-based C ellular A utomata (CarbonVCA) by integrating land use modelling and carbon emission estimation. Shenzhen's cadastral parcel-scale carbon emissions from 2020 to 2060 are predicted as a case study. A good performance was achieved using CarbonVCA (MAPE = 19.017 %, RMSE = 0.175 Mtpa (C)). Three progressive scenarios are designed for carbon emission simulation from land use planning and energy structure restructuring perspectives. The designed carbon emission reduction scenario limits carbon emissions and enables Shenzhen to achieve peak carbon emissions between 2025 and 2030. However, the efforts to reach peak carbon emissions may prompt the relocation of industrial land to the suburbs. Such areas will need appropriate infrastructure construction to break through terrain and landscape constraints, maintain economic growth and achieve sustainable development. This framework can forecast a high spatial resolution of land-use-based carbon emissions, which helps construct low-carbon cities. • CarbonVCA effectively improves the spatial resolution for forecasting carbon emission. • Practical evidence is provided for integrating land use into carbon emission accounting. • The future impacts of diverse policies are predicted to help adjust macro policies. [ABSTRACT FROM AUTHOR]

The development of differentiated emission reduction strategies plays an important role in achieving carbon compliance targets. Each city should adopt carbon reduction strategies according to its carbon emission characteristics. China is a vast country, and there are significant differences between cities. Therefore, this study classifies 340 Chinese cities according to their carbon emission characteristics since 2020 and proposes differentiated emission reduction strategies accordingly. The results of the research show that Chinese cities can be divided into four categories, and they can strive to achieve their carbon peak targets by adopting differentiated emission reduction strategies. In the baseline scenario, Chinese cities will not be able to meet the peak carbon target by 2030. In the differentiated scenario, eco-agricultural cities, industry-led cities, and high-resource-availability cities will be able to achieve peak carbon by 2030. Unfortunately, resource-poor cities will not reach their peak. However, the extent to which their total carbon emissions contribute to the achievement of national goals is low, and their carbon emissions can be traded off for economic development by appropriately relaxing the constraints on carbon emissions. Therefore, in order to achieve China's peak carbon goal, this study proposes emission reduction recommendations that should be adopted by different types of cities to form differentiated emission reduction strategies. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to evaluate the effectiveness of different prefabricated construction (PC) policies using a case study in Wuhan, considering the local context. Design/methodology/approach: The effectiveness of PC policies is falling behind expectations. The main reason lies in an insufficient understanding of the policy impacts. An agent-based model was built by choosing the residential sector in a typical large city of Wuhan, China, as the study case. Different cost reduction scenarios were introduced for investigating the PC policy effectiveness. The proposed model and simulation approach can be used for other cities and generalized to the whole Chinese PC industry with the potential to include more local policies and corresponding data. Findings: Simulation results show that carbon emission reduction will be between 60,000 and 80,000 tons with policy incentives, nearly double that of the no policy intervention scenario. The target of 30% PC in all new buildings by 2026 in China is achievable with the subsidy policies of linear cost reduction, or cost reduction conforms to the learning curve. Practical implications: Simulation results of three kinds of policy show that subsidy policy optimization is necessary regarding reducing the level of subsidy needed. The carbon credit policy is not essential since it has little influence on PC development. Implementing the project procurement restriction policy is not recommended if the scale of development of PC is more important than achieving the development target. Originality/value: This study can help the government and developers make better policy and strategic decisions on PC development and boost the sustainability transition of the construction industry. [ABSTRACT FROM AUTHOR]

Against achieving carbon peaking by 2030 and carbon neutrality by 2060 context in China, the new energy demonstration city policy (NEDCP) has a crucial function to perform in promoting resource utilization efficiency, building the green development policy system, and facilitating carbon emission reduction. However, existing research has rarely investigated the contribution of NEDCP on carbon reduction. To investigate the policy effect of NEDCP, the differences-in-differences (DID) model is introduced to quantify the influence of NEDCP on carbon reduction, taking a statistical sample of 285 Chinese cities over the period 2005–2017 on the basis of exploring the intrinsic mechanism of NEDCP on carbon emissions. The statistical results reveal that NEDCP significantly inhibits carbon emissions. NEDCP's dampening impact on carbon reduction is more pronounced in the eastern area but not in other areas. City size and resource endowment heterogeneity results suggest that NEDCP significantly inhibits the output of carbon emissions in non-resource-based and large cities but insignificantly in resource-based and small- and medium-sized cities. Finally, we conclude that policy-makers should not only broaden the scope of NEDCP implementation continuously but also design relevant policy combination tools following the basic characteristics of each city to provide institutional guarantees for achieving carbon emission reduction. [ABSTRACT FROM AUTHOR]

We work with policymakers in eight cities worldwide to identify technology pathways toward their near- and long-term carbon emissions reduction targets for existing buildings. Based on policymakers' interests, we define city-specific shallow and deep retrofitting packages along with onsite photovoltaic generation potential. Without further grid decarbonization measures, stock-wide implementation of these retrofits in the investigated neighborhoods reduces energy use and carbon emissions by up to 66% and 84%, respectively, helping Braga, Dublin, Florianopolis, Middlebury, and Singapore to meet their 2030 goals. With projected grid decarbonization, Florianopolis and Singapore will reach their 2050 goals. The remaining emissions stem from municipalities not planning to electrify heating and/or domestic hot water use. Different climates and construction practices lead to varying retrofit packages, suggesting that comparable technology pathway analyses should be conducted for municipalities worldwide. Twenty months after the project ended, seven cities have implemented policy measures or expanded the analysis across their building stock. Here, the authors perform analysis of technology pathways for existing buildings using urban building energy models developed with cities, showing that shallow and deep retrofits along with onsite photovoltaic and grid decarbonization can help achieve carbon reduction targets. [ABSTRACT FROM AUTHOR]

The digital economy has great potential to boost innovation and social productivity, and it plays an important role in helping to achieve carbon peak and carbon neutrality. This study focuses on analyzing and testing the role of the digital economy in promoting carbon reduction in Chinese cities. Based on panel data of 282 cities in China from 2011 to 2019, this study measured the development level of the digital economy and carbon emission intensity by constructing a digital economy development level evaluation index system and a carbon emission inversion model, respectively. It was found that the digital economy can significantly reduce urban carbon emissions by promoting industrial structure upgrading and green innovation, and the digital economy will have a more obvious promotion effect on urban carbon emission reduction over time. This conclusion was found to be valid after we performed robustness tests such as the instrumental variable method, quasi-experimental analysis, and placebo test. Furthermore, heterogeneity analysis showed that eastern, regenerative, and provincial capital cities are better able to promote carbon emission reduction under the development of the digital economy. This study provides new empirical evidence at the city level for developing the digital economy to reduce urban carbon emissions and acts as a useful reference for developing countries to realize "smart carbon emission reduction". [ABSTRACT FROM AUTHOR]

Urban areas afford 56% of the world population and the top 600 cities emit 70% of the world greenhouse gases, highlighting enormous challenges and potentials of carbon emission reduction and sustainability in high-density cities. Though vast research has reviewed the green building assessment (GBA) systems in different perspectives, little has explicitly examined the dialectics of GBA, particularly its complexity and dynamics. The assessment of green buildings, however, can be regarded as a complex dynamic system with multifaceted dialectics, particularly in high-density cities. Thus, this paper aims to examine the dialectics of GBA within the context of high-density cities by identifying 42 GBA systems and then comparing 12 widely adopted systems in depth. Dialectics denote the complex and dynamic interdependency among the elements of a system. A dialectical system framework is developed to guide the systematic comparison of the GBA systems in three dimensions: 'concept', 'methodology' and 'value'. The results reveal that dialectics exist and encounter challenges in all three dimensions, including a multi-perspective but inconsistent concept of GBA, well-organised but oversimplified methodology for GBA, and value-laden but insufficient stakeholder engagement in GBA. The developed framework provides a new approach to understanding the complex and dynamic interdependency among the various elements of GBA systems. The findings should raise the awareness of green building developers, planners and designers about the dialectics in GBA and thus inform the associated decision making and design optimisation, making it possible to more effectively achieve green buildings. • A dialectical system framework is proposed to examine green building assessment (GBA). • The dialectics are elaborated in the dimensions of concept, methodology and value. • Dialectics are found to exist and encounter challenges in all the three dimensions. • 42 GBA systems are identified with 12 compared in depth in high-density city contexts. • The framework provides a new approach to understand the complexity and dynamics of GBA. [ABSTRACT FROM AUTHOR]

A one-way traffic strategy is widely used in densely populated urban areas to improve traffic efficiency, but its environmental impacts remain largely unclear. This study investigated the effects of a one-way traffic strategy on vehicle emissions along a busy traffic corridor in Xi’an, China. This was done by integrating the traffic flow model calibrated by onsite traffic data and the emission model. The detour distance estimation method was proposed so that the emission trade-offs between increased vehicle miles traveled (VMTs) and improved traffic efficiency on one-way traffic corridors could be uncovered. Empirical results showed that one-way traffic cut down the total CO2 emissions by 11.8%, and reduced the CO, hydrocarbon (HC), and NOx emissions by 4.7%–25.0%. However, such emission reduction capacity gradually weakened and even completely dissipated with the increase of traffic demand on the corridor. When the road volume-tocapacity ratio (V/C > 0.87), one-way traffic was no longer a greener traffic organization. With the promotion of mitigation policies and technologies, one-way traffic is expected to further reduce CO2 emissions by 4.7%–10.9% and CO, HC, and NOx emissions by 8.4%– 41.1%. In addition, there was a total of 456 vehicles detours with an overall detour distance of 316.8 km, which resulted in a doubling of the emissions of detour vehicles. Although the implementation of one-way traffic brings environmental benefits, the detour problem should also be taken seriously. This study provides convincing evidence for understanding the emission changes on traffic corridors caused by one-way traffic conversion to support the promotion of eco-friendly traffic organization for realizing the goal of carbon peaking. [ABSTRACT FROM AUTHOR]

This study attempts to analyze the impact of population, property, technology, energy factors, and spatial agglomeration in the logistics industry on carbon emissions. To achieve the goal of peak carbon and carbon neutrality, the relationship between influencing factors and carbon emissions was analyzed based on panel data from the logistics industry for 30 provinces in China from 2003 to 2017 using an improved STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model and a spatial lag model (SLM). The results show that population, property, technology, and energy factors in the logistics industry all have different degrees of influence on carbon emissions, wherein population, energy, and property have a greater influence, which implies that carbon emission reduction policies can be carried out considering the relevant aspects. In addition, under the influence of spatial agglomeration, the degree of influence of freight mileage (FM), total fixed-asset investment (TFAI), and industry population (IPOP) on carbon emissions decreases, and the degree of influence of energy intensity (EI) and industry per capita GDP (IPCG) increases. This suggests that corresponding emission reduction policies should be formulated for large urban areas based on technological innovation, infrastructure, and talent training, while smaller urban areas can focus on developing new energy and industrial economies. These findings help to complement the existing literature and provide policymakers with some insights related to urban logistics development. [ABSTRACT FROM AUTHOR]