Nonlinear influence of urbanization on China's urban residential building carbon emissions: New evidence from panel threshold model

Is renewable energy development endangering power supply reliability?

The development of renewable energy is indispensable to promoting the low-carbon transition of power systems. Nevertheless, it also brings uncertainty to the reliability of power systems. Herein, the panel model and panel threshold model are established based on the provincial data in China from 2012 to 2020. The results reveal that the negative effect of renewable energy development (RED) on power supply reliability (PSR) is gradually lessening. If the development of renewable energy is a rational way, power supply reliability can be improved. Additionally, energy-exporting regions bear more risks of RED than energy-importing regions. If the coal prices are stable and natural disasters are manageable, the RED can enhance the PSR. However, if they are not stable or controllable, a high proportion of renewable energy in the power system could cause even more severe problems with PSR. Based on these critical results, some suggestions are made to promote the formation of a new power system.

Spatio-temporal evolution and driving factors of new urbanization in central China based on multi-source data

Urbanization is an inevitable outcome of the development of human society to a certain stage, and it is also an irreversible pattern of the concentration degree of human society. Based on multi-source data such as remote sensing images, ecological environment and socio-economic data, the evaluation index system of new urbanization is constructed from multi-dimensions of population, economy, society, space and ecology. To explore the spatio temporal evolution and driving factors of urbanization in 80 prefecture-level cities in central China from 2013 to 2021 by using entropy method, spatial autocorrelation model and geographic detector. The results show that: (1) The level of new urbanization continues to grow, with the average value rising from 0.1562 in 2013 to 0.2557 in 2021, and the regional differences are obvious, forming a circle structure with Wuhan, Zhengzhou and other provincial capitals as the center and weakening radiation to surrounding cities. (2) The agglomeration of ecological urbanization is significant, and the agglomeration trend is gradually enhanced. The high-high agglomeration areas tend to Xinzhou City, most prefecture-level cities in Hubei Province and some prefecture-level cities in Southern Hunan Province, while the low-low agglomeration areas tend to Changzhi City, most prefecture level cities in Henan Province and some prefecture-level cities in Northern Anhui Province. (3) The night light index, total retail sales of consumer goods, investment in fixed assets, proportion of built-up areas and urban economic density are the main driving factors affecting the level of new urbanization. (4) The interaction of driving factors shows double factor enhancement and nonlinear enhancement effects.

Threshold effect of ecosystem services in response to human activity in China's urban agglomeration: a perspective on quantifying ecological resilience

Ecological resilience reflects the role of human activity intensity (HMI) on regional ecosystem services (ESs), and resilience improvement is crucial for the high-quality development of urban agglomeration areas. However, a theoretical framework for ecological resilience needs to be developed based on ES thresholds under human activities. Based on the threshold index, we used threshold regression model to determine of the nonlinear dominant factors affecting ESs and to identify the priority areas for ecological restoration. The results suggest that (1) CS, HQ, and TES declined, while FP increased. The spatial distribution of each ES showed higher values in the central region and lower values in the surrounding areas. HMI showed a significant upward trend, with expanding high-HMI areas. There is a threshold effect in the relationship between HMI and ESs, leading to variations in their positive, non-monotonic, or non-linear interactions. (2) HMI positively and significantly affects ESs, especially at low threshold levels. The effect of HMI on ESs is negative in counties with higher threshold levels rather than in lower-level grids. (3) The critical area of artificial potential ecological restoration was 712 km2, primarily concentrated around urban; the critical area of natural restoration was 490 km2. Therefore, the threshold in the relationship between HMI and ESs should be given more attention. This study serves as a guide for picking out key regions for territorial ecological restoration.

How does new-type urbanization affect total carbon emissions, per capita carbon emissions, and carbon emission intensity? An empirical analysis of the Yangtze River economic belt, China

The coordination of the relationship between new-type urbanization (NTU) and carbon emission reduction has become China's primary strategic goal. However, previous studies on this topic mainly examined the unidirectional impact of NTU on carbon emissions, while disregarding their potential relationship. This study establishes an evaluation system for measuring NTU and explores the bidirectional impact between NTU and total carbon emissions (TCE), per capita carbon emissions (PCE), and carbon emission intensity (CEI). The Yangtze River Economic Belt of China is selected as the study area, and the period from 2005 to 2019 is studied. The results show that: (1) The NTU levels in the upstream, midstream, and downstream regions of the Yangtze River increase from 0.148, 0.208, and 0.365 in 2005 to 0.465, 0.503, and 0.675 in 2019, suggesting that NTU levels tend to be balanced in these three reaches. (2) A positive bidirectional impact is found between NTU and TCE, as well as PCE in midstream and upstream regions, whereas in downstream regions, only a positive unidirectional effect of NTU on TCE and PCE is found. (3) Specifically, TCE plays the most significant role in promoting NTU in upstream regions, while NTU exerts the greatest pulling force on TCE and PCE in midstream regions. (4) Unlike the positive impact between NTU and TCE or PCE, there is a significant two-way inhibitory effect between NTU and CEI. (5) A three-step carbon emission reduction law is found in the process of NTU, where NTU towards low carbon development will experience NTU inhibits CEI, PCE, and TCE in sequential order. These findings provide an important reference for promoting a harmonious relationship between NTU and carbon emission reduction, helping governments formulate reasonable measures to achieve high-quality urban development and carbon emission reduction goals.

New urbanization and carbon emissions intensity reduction: Mechanisms and spatial spillover effects

The advancement of new urbanization policy (NU) and the attainment of double carbon targets play pivotal roles in facilitating high-quality economic development in China. This paper conducts a comprehensive analysis of the mechanism and spatial spillover effects of NU on carbon emission intensity reduction (CEIR), building upon an examination of the nature of NU and the principles of urban carbon pollution control. The research employs a multi-period difference-in-difference model (DID) to explore the causal relationship between NU and CEIR, using panel data from 278 prefecture-level cities spanning the period of 2006 to 2020. Empirical results demonstrate that the implementation of NU resulted in an 8.4 % reduction in carbon emission intensity (CEI). Furthermore, the analysis of the transmission mechanism reveals that NU stimulates green technology innovation and facilitates the development of industrial agglomeration, thus achieving CEIR. The decomposition of the spatial Durbin model indicates significant spatial spillover effects in the effectiveness of NU, signifying its positive impacts not only within the region but also in generating benefits for surrounding areas. Moreover, the dynamic heterogeneity results indicate that entrepreneurial vitality and urbanization rate exhibit dynamic effects on the policy's CEIR effect, both displaying nonlinear enhancement curves. Based on this, the policy implications of this paper include: The government should enhance regional coordinated governance to address carbon emissions pollution in alignment with China's NU. This can be accomplished by effectively harnessing the driving role of green innovation and industrial agglomeration. Additionally, the local government can actively create an entrepreneurial atmosphere and expedite the urbanization process in order to support NU in the implementation and achievement of CEIR.

Complexity influence of societal development comprehensive indicators on building carbon emission: empirical evidence from China

Carbon mitigation in the building sector is crucial for China to fulfill its commitments towards achieving a carbon peak and carbon neutrality. However, the impact of societal development and ecological indicators on building carbon emissions remains unclear. This study employs the panel smooth transition regression model to investigate the complex implications of societal development comprehensive indicators, characterized by harmonious development, decoupling, and technological advances, on buildings' total carbon emissions, based on the evidence from China's 30 provinces for the period between 2007 and 2020. Additionally, the robustness of the model confirms that the conclusion is still valid. The empirical results indicate a strongly non-linear relationship between societal development comprehensive indicators and building carbon emissions. Both the harmonious development and technological advances exhibit two transition functions, and decoupling features a single transition function. Harmonious development is more sensitive to the impact of building carbon emissions, while technological advances have tremendous emission reduction potential. From the time dimension, fluctuation trends and ranges are different. From the spatial dimension, the inhibiting and promoting effects on each province have regional heterogeneity. Our results entail suggestions for reduced building total carbon emissions and practical strategies for regional climate resilience and efficiency in mitigating climate change.

Exploring the coupling and decoupling relationship of urbanization and carbon emissions in China

Burgeoning urbanization is a defining challenge for global carbon emissions mitigation in the coming decades. In this context, achieving low-carbon urbanization remains an urgent issue that demands prompt solutions. The coupling and decoupling relationships between urbanization and carbon emissions play an important role in the coordination of urbanization development and carbon emissions reduction, which has rarely been explored in existing studies, especially in China at the county level. To address this gap, the coupling and decoupling relationship between the urbanization level (UL) and carbon emissions density (CED) was explored using an improved coupling degree model and the Tapio decoupling method in China at the county level from 2000 to 2020. The results showed that the UL and CED of China both exhibited increasing trends, and the spatial distribution was quite similar, with the UL increasing from 0.018 in 2000 to 0.028 in 2020 and the CED increasing from 95.163 ton/km2 in 2000 to 295.746 ton/km2 in 2020. The spatial distribution of hotspots in the UL change differed with time, whereas that in the CED change was relatively stable. However, both of them were concentrated in eastern China. The coupling degrees of the UL and CED in China were 0.348, 0.355, 0.369, 0.370, and 0.338 in 2000, 2005, 2010, 2015, and 2020, respectively, with the moderately unbalanced type (>35%) being dominant at the county level and mainly scattered in eastern China. The changes in the spatial distribution patterns of the 10 subcategories were quite limited, with the systematically balanced type being dominant (89%). The decoupling types of the UL and CED during 2000-2005, 2005-2010, and 2010-2015 were weak decoupling, while those in 2015-2020 were expansive negative decoupling. At the county level, the most significant transition occurred between expansive negative decoupling, strong decoupling, and strong negative decoupling. The proportion of strong decoupling type counties peaked in 2015-2020 (70.86%), whereas that of the strong negative decoupling type counties remained high (17.55%), scattering the country. These findings can advance policy enlightenment of low-carbon urbanization and green development for China against the backdrop of "30·60 dual carbon" goal.

Building carbon peak scenario prediction in China using system dynamics model

As the issue of global climate change caused by carbon emissions is of great concern, China has proposed achieving its achieve carbon peak goal by 2030. Building carbon emissions account for approximately 50% of China's total carbon emissions. It is crucial to study the time and values of building carbon peaks. In this paper, based on a system dynamics model, logarithmic mean Divisia index model and Monte Carlo simulation, we predict the building carbon peak in China. The following conclusions are obtained: 1) in the baseline scenario, China's building carbon emissions will peak at 5,427 million tons in 2027. In the high-speed development scenario, China's building carbon emissions will peak at 6,298 million tons in 2032. In the coordinated development scenario, the green development scenario, the low-carbon development scenario, and the low-speed development scenario, the peak occurs in 2030 at 5,972 million tons, 5,991 million tons, 5,657 million tons, and 6,329 million tons, respectively. 2) According to the comprehensive simulation, China's building carbon emissions will reach the peak in 2030, with an 80% probability of reaching 5,729-6,171 million tons.

Investigation of resource curse hypothesis: the role of renewable energy and urbanization in realizing environmental sustainability in China

This research investigates the resource curse hypothesis and environmental sustainability by integrating China's natural resources, renewable energy, and urbanization. However, the EKC N shape describes the complete picture of the EKC hypothesis for the growth-pollution relationship. The findings of FMOLS and DOLS show that economic expansion positively drives carbon dioxide emissions in the beginning, then negatively so after the target level of growth is reached. Continuing economic expansion in China does not maintain the intended level and again has a beneficial impact on the country's carbon dioxide emissions. However, the EKC U, inverted U, and N shapes persist in the growth-pollution connection over the long term. Although adopting renewable energy and urbanization help reduce carbon dioxide emissions, the formation of fixed capital worsens environmental conditions. Natural resource rents are a major cause of environmental degradation and the resource curse that has plagued China. Economic growth, as well as its square and cube, has a causal effect on CO2 emissions, as shown by the frequency domain causation. Carbon dioxide emissions at frequencies of 0.05, 1.50, and 2.50 are momentarily predicted by the use of renewable energy and urbanization. The investigation recommends switching to renewable energy sources owing to low cost and the potential to limit the overuse of non-renewables. To balance the overdo of natural resources and ensure continued long-term growth-environment sustainability, technological advancement is recommended as a countermeasure as a mitigating mechanism.

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

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

Research on economic and social impact of carbon emissions in the construction industry at a regional scale: a case of Shandong, China

To identify the impact of carbon emissions in the construction industry on the society and economy at a regional scale is of great significance for the sustainable development of the construction industry. Taking Shandong, China, as an example, this study uses a process-based carbon emission assessment method to calculate the carbon emissions of the construction industry in Shandong from 2010 to 2020. Based on specific situations at different phases, ten major factors affecting the society and economy have been analyzed and identified. Then, the Seemingly Uncorrelated Regression (SUR) model was used to analyze carbon emissions and economic and social factors at each stage. The results show the following: (1) The overall carbon emissions of the construction industry in Shandong are on an increasing trend, the carbon emission intensity is on a decreasing trend, and the material production and operation stages are the main source of carbon emissions; (2) There is a significant relationship between carbon emissions in the construction phase and all influencing factors, so it should be the main phase for macro-regulation; (3) Unlike in the Ordinary Least Square (OLS) model, the error terms of the gross output value of the construction industry and the total turnover of the main building materials equations are independent; however, there is an interaction between the error terms of the remaining equations. The results provide a theoretical reference for governmental departments to set up environmental targets and make policy regulations.

Matching degree evaluation between new urbanization and carbon emission system in China: a case study of Anhui Province in China

In order to reveal the relationship between new urbanization and carbon emission to provide reference opinions for the construction of low-carbon urbanization, an evaluation system between new urbanization and carbon emission was constructed. Then their matching degree relationship was analyzed by coupling coordination degree model based on the data from 2012 to 2021 in Anhui Province, and their development trend from 2023 to 2032 was predicted by gray prediction model. The results show that: (1) New urbanization and carbon emission have the co-trend effect, and the consistency of core impact factors is relatively significant. Among them, the level of new urbanization increases from 0.058 in 2012 to 0.699 in 2021 and carbon emission development increases from 0.023 in 2012 to 0.165 in 2021, which both showing an upward trend. Meanwhile, social urbanization and population carbon emission are the core influencing factors. (2) The coupling coordination degree between new urbanization and carbon emission is low, but the synergy trend is optimistic and there is a large room for improvement. Among them, the coupling coordination coefficient of the coupling system rises from 0.136 in 2012 to 1.412 in 2021 (antagonistic phase), and then reaches 0.820 by 2032 (highly coordinated phase) by forecast. It shows that their current development is unbalanced, but the development trend is good, and there is a chance for improvement. This paper deepens the understanding of the logical correlation between new urbanization and carbon emission, and the following views are formed: (1) Low-carbon development is still the mainstream of new urbanization; (2) The coordination development of new urbanization and carbon emission reduction should be strengthened.

GHG emissions and energy consumption of residential buildings-a systematic review and meta-analysis

Residential buildings generate significant greenhouse gas (GHG) emissions and consume energy throughout their life cycle. In recent years, research on GHG emissions and energy consumption of buildings has developed rapidly in response to the growing climate change and energy crisis. Life cycle assessment (LCA) is an important method for evaluating the environmental impacts of the building sector. However, LCA studies of buildings show widely varying outcomes across the world. Besides, environmental impact assessment from a whole life cycle perspective has been undeveloped and slow. Our work presents a systematic review and meta-analysis of LCA studies on GHG emissions and energy consumption in the preuse, use, and demolition stages of residential buildings. We aim to examine the differences among the results of diverse case studies and demonstrate the spectrum of variations under contextual disparities. Results show that residential building emits about 2928 kg GHG emission and consumes about 7430 kWh of energy per m² of gross building area on average throughout the life cycle. Residential buildings have an average GHG emission of 84.81% in the use phase, followed by the preuse phase and demolition phase; the mean energy consumption in the use stage occupied the largest share of 84.52%, followed by preuse stage and demolition stage. GHG emissions and energy use vary significantly in different regions due to different building types, natural conditions, and lifestyles. Our study stresses the compelling requirement to slash GHG emissions and optimize energy consumption from residential buildings by use of low carbon building materials, energy structure adjustment, consumer behavior transformation, etc.

The heterogeneity impact of factor market distortions on GTFP: from the view of resource-based cities

Improving the efficiency of factor allocation, breaking the resource curse, and achieving high-quality economic development are an urgent concern in resource-based cities. Using the panel data of 116 prefecture-level resource-based cities in China from 2005 to 2020, this paper constructs the data envelopment analysis-Malmquist index model to measure the level of high-quality economic development; to construct a resource misallocation growth accounting model based on the total production function to measure the distortion coefficient, the threshold panel model is employed to explore the threshold effect of factor market distortion on high-quality economic development. The results yielded three important findings: (1) The green total factor productivity of resource-based cities in China presents spatial heterogeneity and type heterogeneity. (2) Factor market distortion gradient standards are proposed innovatively, and the factor market distortion level shows the spatial heterogeneity and type heterogeneity. (3) The influence of factor market distortion on high-quality economic development in resource-based cities presents a double threshold effect. According to the above research conclusions, this paper gives several policy recommendations to promote the factor marketization allocation and high-quality economic development of resource-based cities.

Impacts of financial development on the energy consumption in China from the perspective of poverty alleviation efficiency

Poverty alleviation and energy saving are two major issues of sustainable development targets. Meanwhile, financial development (FD) is a powerful engine of economic growth, which is regard as a valid approach to contain the demand for energy consumption (EC). However, few studies link the three factors and explore the specific impact mechanism of poverty alleviation efficiency (PE) on the tie between FD and EC. Thus, we employ the mediation and threshold models to evaluate the influence of FD on the EC in China during 2010-2019 from the perspective of PE. We affirm that FD indirectly promotes EC through the channel of PE. The mediating effect of PE is responsible for 15.75% of the total effect of FD on the EC. Moreover, FD generates a significant threshold impact on the EC considering the change of PE. When the PE exceeds 0.524, the role of FD in promoting EC is strengthened. Ultimately, the outcome suggests policymakers need to prominent the trade-off between energy saving and poverty reduction during the fast evolution of financial system.

Can China's carbon trading policy improve the profitability of polluting firms: a retest of Porter's hypothesis

The future trends and development trajectory of China's carbon emissions trading scheme (ETS), one of the key policy instruments for curbing peak carbon emissions and achieving carbon neutrality, have drawn a lot of interest. However, the Porter hypothesis (PH) and its validity boundary have not been explored sufficiently. We use micro-firm data from 2010 to 2019 to investigate whether the triple-difference (DDD) method could reveal the weak PH on the policy viewing ETS as a quasi-natural experiment in this work. Meanwhile, we use the panel threshold model and the moderated mediation effect model to assess the scientific border of the PH on the ETS. The findings show that by verifying the weak PH, the ETS may greatly enhance investment and foster the inventiveness of heavy-polluting industries (HPE). In contrast, the strong PH on the ETS has unstable validity and has non-linear characteristics. In particular, the ETS shows a U-shaped link between innovation and profitability by first decreasing and then increasing HPE's profitability through R&D. The cost of R&D and compliance costs being combined negatively impacts HPE's profitability. Further analysis shows that ETS will have different effects on the profitability of HPE due to R&D level and the threshold change of the compliance cost. This paper will offer some insightful points of view for the implementation of carbon market mechanisms in developing nations like China.

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

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

Exploring the low-carbon transition pathway of China's construction industry under carbon-neutral target: A socio-technical system transition theory perspective

The carbon emissions growth in the construction industry hinders the achievement of global carbon-neutral target, especially in China. Studies suggest that developing low-carbon technologies is an effective means of achieving the low-carbon transition (LCT) in the construction industry. However, these studies ignore the fact that the LCT is a complex and systemic issue that needs to consider the interaction of technical and non-technical factors. Thus, based on the socio-technical system transition theory, this study identified the influencing factors and constructs a dynamics model to simulate the dynamic changes of the LCT in China's construction industry under different scenarios. The results showed that multi-level factors coordinated to drive the LCT of the construction industry. Environmental factors played a weak role and the effectiveness of government intervention decreased with the transition process. On the contrary, technological and market factors were indispensable drivers and especially played a dominant role during the later stages of the transition. Finally, the LCT pathway of China's construction industry was proposed based on the results. These findings expand the boundary of theoretical research on industrial transition and provide a decision-making reference for the advancement of international carbon-neutral work.

Research Progress and Hotspot Analysis of Residential Carbon Emissions Based on CiteSpace Software

Residential carbon emissions are one of the critical causes of climate problems such as global warming. It is significant to explore the development and evolution trend of residential carbon emissions research for mitigating global climate change. However, there have been no studies that comprehensively review this research field. Based on the research papers on residential carbon emissions included in the Web of Science core database and China National Knowledge Infrastructure database, the CiteSpace bibliometric analysis software was used in this paper to draw the visual knowledge map of residential carbon emissions research and reveal its research status, research hotspots, and development trend. We found that residential carbon emissions research has gone through the stage of "emergence-initiation-rapid development", and the research in the United States and the United Kingdom has played a fundamental role in developing this research field. Research hotspots mainly focus on analyzing energy demand, quantitative measurement, and impact mechanisms of residents' direct and indirect carbon emissions and low-carbon consumption willingness. The focus of research has gradually shifted from qualitative analysis based on relevant policies to the analysis of quantitative spatiotemporal measurements and drive mechanisms of direct and indirect carbon emissions from residential buildings, transportation, and tourism based on mathematical models and geographic information system technologies. Modern intelligent means such as remote sensing technology and artificial intelligence technology can improve the dynamics and accuracy of this research, but there are few related types of research at present. Based on these research status and trends, we proposed that the future research direction of residential carbon emissions should focus more on spatial analysis and trend prediction based on intelligent methods under a low-carbon background.

How do information and communication technology and urbanization affect carbon emissions? Evidence from 42 selected "Belt and Road Initiative" countries

Reducing carbon emissions is key to achieving the 13th UN sustainable development goals. With the acceleration of informatization and urbanization in the "Belt and Road Initiative" (BRI) countries, it is necessary to explore the impact of ICT and urbanization on carbon emissions in the BRI countries. This paper uses the Driscoll-Kraay panel regression method, multi-chain mediation effect model, and panel moment quantile regression method to study the influence channel and heterogeneous impact of ICT, urbanization, and their interaction on carbon emissions in 42 selected BRI countries. The main empirical results reveal the inhibition of ICT and the promotion of urbanization on carbon emissions. Moreover, the integrated development of ICT and urbanization contributes to reducing carbon emissions. Industrial structure upgrading and energy intensity are found to be the channels through which ICT, urbanization, and their interactions affect carbon emissions. In addition, the impact of ICT, urbanization, and their interaction on carbon emissions varies with different measurement indicators and quantiles. Therefore, it is suggested that BRI countries should formulate appropriate ICT development policies according to their conditions, strengthen ICT application, and especially promote the integrated development of ICT and urbanization, to achieve sustainable urban development.

Spatially Non-Stationary Response of Carbon Emissions to Urbanization in Han River Ecological Economic Belt, China

Within the context of the "30·60 dual carbon" goal, China's low-carbon sustainable development is affected by a series of environmental problems caused by rapid urbanization. Revealing the impacts of urbanization on carbon emissions (CEs) is conducive to low-carbon city construction and green transformation, attracting the attention of scholars worldwide. The research is rich concerning the impacts of urbanization on CEs but lacking in studies on their spatial dependence and heterogeneity at multiple different scales, especially in areas with important ecological statuses, such as the Han River Ecological Economic Belt (HREEB) in China. To address these gaps, this study first constructed an urbanization level (UL) measurement method. Then, using a bivariate spatial autocorrelation analysis and geographically weighted regression model, the spatial relationships between UL and CEs from 2000 to 2020 were investigated from a multiscale perspective. The results were shown as follows. The total CEs in the HREEB witnessed an upsurge in the past two decades, which was mainly dispersed in the central urban areas of the HREEB. The ULs in different regions of the HREEB varied evidently, with high levels in the east and low levels in the central and western regions, while the overall UL in 2020 was higher than that in 2000, regardless of the research scale. During the study period, there was a significant, positive spatial autocorrelation between UL and CEs, and similar spatial distribution characteristics of the bivariate spatial autocorrelation between CEs and UL at different times, and different scales were observed. UL impacted CEs positively, but the impacts varied at different grid scales during the study period. The regression coefficients in 2020 were higher than those in 2000, but the spatial distribution was more scattered, and more detailed information was provided at the 5 km grid scale than at the 10 km grid scale. The findings of this research can advance policy enlightenment for low-carbon city construction and green transformation in HREEB and provide a reference for CE reduction in other similar regions of the world.

Digital economy, energy efficiency, and carbon emissions: Evidence from provincial panel data in China

Improving energy efficiency and lowering carbon emissions are of great importance to realize the "dual carbon" goal of carbon peak and carbon neutrality. Digital economy is a new engine of economic development, but whether or how it affects energy efficiency and carbon emissions are unclear. Utilizing panel data of China's 30 provinces from 2012 to 2019, this study empirically explores the relationships among digital economy, energy efficiency, and carbon emissions. Meanwhile, from the perspective of energy efficiency, applying mediation models and panel threshold model, it analyzes the direct, indirect, and nonlinear influencing mechanisms of digital economy on carbon emissions. The results reflect that the development of digital economy in China intensifies carbon emissions. Energy efficiency serves as a vital partial mediator between the two. The enhancement of energy efficiency can lower carbon emissions. However, the development of digital economy is not conducive to improving energy efficiency, thereby, indirectly increasing carbon emissions. The mediating effect of energy efficiency accounts for 30.58 % of the total effect of digital economy on carbon emissions. Meanwhile, taking energy efficiency into account, the impact of digital economy on carbon emissions has a significant double-threshold effect and presents an N-shaped trend. [0.824, 0.912] is the optimal range of energy efficiency, within which the growth of the digital economy can empower carbon emission abatement to some extent. In addition, the expansion of population size, the coal-based energy consumption structure, and the industrial structure significantly increase carbon emissions. The improvements in living standards and environmental regulations can help to decrease carbon emissions, but the emission abatement effects are not significant. Those conclusions reveal the importance of optimizing the level and quality of digital economy and adopting differentiated digital economy development policies based on energy efficiency to achieve carbon emission reduction.

Coupling coordination degree and driving factors of new-type urbanization and low-carbon development in the Yangtze River Delta: based on nighttime light data

The coordination relationship between new-type urbanization and urban low-carbon development under the goal of carbon neutrality has become a hot issue that needs to be focused on when formulating policies. Based on the estimation of urban CO2 emissions by night light data, this study used spatial autocorrelation, spatial Markov chain and geographically weighted regression model to measure the spatial correlation and spillover effects of the coupling coordination degree of two systems in the Yangtze River Delta urban agglomeration from 2005 to 2018 and analyzed the influencing factors. The results showed that (1) the coupling coordination degree showed an increasing trend, but the club effect was quite obvious, and the regional pattern was higher in southeast and lower in northwest; (2) the spatial spillover effect of coupling coordination degree is significant, which aggravates the long-term persistence of the imbalance pattern; (3) regional economic level, government fiscal regulation, and industrial upgrading are the main driving forces for the increase of coupling coordination degree, while over-concentration of population and low energy efficiency are the main obstacles. Finally, on the basis of these conclusions, we provide targeted policy planning suggestions for policy makers.

Industrial Coagglomeration, Green Innovation, and Manufacturing Carbon Emissions: Coagglomeration's Dynamic Evolution Perspective

The achievement of China's low-carbon development and carbon neutrality depends heavily on the decrease of manufacturing carbon emissions. From coagglomeration's dynamic evolution perspective, by using panel-threshold-STIRPAT and mediation-STIRPAT models, this study examines the relationships among industrial coagglomeration, green innovation, and manufacturing carbon emissions and explores the direct and indirect function mechanisms. Panel data of China's 30 provinces from 2010 to 2019 are employed. The results imply that, first, the impact of industrial coagglomeration on manufacturing carbon emissions is nonlinear and has significant threshold effects. Industrial coagglomeration negatively affects manufacturing carbon emissions, and as the coagglomeration level deepens, the negative effect has a diminishing trend in marginal utility. Once the coagglomeration degree exceeds a certain threshold, the negative impact becomes insignificant. At present, for 90% of China's regions, an increase in industrial coagglomeration level can help reduce manufacturing carbon emissions. Second, green innovation is a vital intermediary between industrial coagglomeration and manufacturing carbon emissions. It is a partial intermediary when industrial coagglomeration is at a relatively lower-level stage and a complete intermediary when industrial coagglomeration is at a relatively higher-level stage. These findings reveal the significance of optimizing industrial coagglomeration and the level and efficiency of green innovation to decrease carbon emissions.

Spatial correlation network structure of China's building carbon emissions and its driving factors: A social network analysis method

Clarifying the spatial association network of provincial building carbon emissions and its influential drivers is profoundly significant for transregional collaborative emission reduction and regionally-coordinated development. This study adopts the social network analysis method to investigate the network structure characteristics of carbon emissions in the building sector based on China's provincial-level evidence from 2000 to 2018. Then, the quadratic assignment procedure is further utilized to examine the driving factors. The results demonstrate that building carbon emissions in China take the form of a network structure. From 2000 to 2018, the relevance and stability of the spatial associations gradually strengthened. Shanghai, Jiangsu, Tianjin, Beijing and Zhejiang are in the center of the spatial association network and play a vital part in the network. The network of carbon emissions in the building sector can be classified into four plates: the main inflow plate, main outflow plate, bidirectional spillover plate and agent plate. Geographical adjacency, economic development level, energy intensity and industrial structure are significantly correlated with building carbon emissions. The urbanization level has no significant influence on the spatial correlations of building carbon emissions. This study is conducive to formulating energy conservation policies and promoting transregional collaborative emission reductions.

Spatiotemporal characteristics and influencing factors of carbon emissions from civil buildings: Evidence from urban China

Carbon emissions from civil buildings refer to the carbon emissions generated during the operation of civil buildings. With the continuous development of the urban economy and the improvement of people’s living standards, this part of carbon emission puts tremendous pressure on China to achieve the goal of carbon peaking and carbon neutral. In the context of rapid urbanization, studying the spatiotemporal characteristics and influencing factors of the carbon emissions from civil buildings have strong practical significance for China to achieve the "dual carbon" goal. Based on the emission data from 104 prefecture-level cities in China, we examine the spatiotemporal characteristics of the civil building carbon emissions from the perspectives of temporal evolution trend, spatial distribution and its dynamic evolution, spatial difference and its decomposition, and spatial autocorrelation characteristics. Finally, we reveal the influencing factors of the carbon emissions from civil buildings using static panel data models and spatial dynamic panel data models. The results of the study show that: (1) During the sample period, the carbon emissions from civil buildings have increased year by year. The civil building carbon emissions have become an important source of China’s overall carbon emissions. Realizing energy saving and emission reduction in the operational stage of civil buildings is crucial to realizing China’s "dual carbon" goal. (2) According to the estimated results, there is a significant inverted U-shaped non-linear relationship between urbanization and civil building carbon emissions. Most Chinese cities are located in the upward part of the inverted U-shaped curve at present. Thus, the traditional economic growth model characterized by high energy consumption and high emission during rapid urbanization should be abandoned to reduce the carbon emissions from civil buildings. (3) Technological progress and fixed asset investment can effectively reduce the carbon emissions from civil buildings. At the same time, the level of marketization and social consumption expenditure positively affect the carbon emissions from civil buildings. It is necessary to improve the relevant market mechanisms, policy subsidies, and other means to encourage the application of green energy-saving technologies in civil buildings. Also, it is needed to guide the urban residents’ consumption structure and lifestyle in a low-carbon direction, to reduce the energy consumption and carbon emissions during the operation of civil buildings.

Toward Sustainable Development: Unleashing the Mechanism Among International Technology Spillover, Institutional Quality, and Green Innovation Capability

Under the background of sustainable development, China's economic growth engine becomes innovation-driven, and it is an important way for China to rapidly improve its green innovation capability by opening up to the outside world and utilizing the spillover effect of international technology. In this article, the system quality evaluation system is reconstructed by the method of fully arranged polygonal graphical indicators, and the provincial system quality in China is measured and added into the model as a regulating variable. The dynamic panel method and the dynamic threshold panel method are used to test the direct effects of foreign direct investment (FDI) and foreign trade on green innovation capability, the interaction effect of institutional quality, and the threshold effect. Empirical results show that the three technology spillovers have significantly promoted China's green innovation capability. System quality will affect the determining coefficient of international technology spillovers on China's green innovation capability. The positive promoting effects of FDI and foreign trade on China's green innovation capability, all increase with the improvement of China's system quality. Therefore, when utilizing FDI and foreign trade to promote green innovation in each region, each region should consider creating a good institutional environment for the emergence of international technological effects.

Multidimensional Impact of Urbanization Process on Regional Net CO2 Emissions: Taking the Yangtze River Economic Belt as an Example

Urbanization is a powerful symbol and an inevitable human economic and social development trend. This process affects carbon dioxide emissions by changing the human output and lifestyle and encroaches over the carbon sink areas by adjusting the land use types impacting the regional carbon balance. We systematically analyzed the influence of urbanization on regional net CO2 emissions (NCE) and built a quantitative model for the impact of urbanization on NCE based on population, economy, and land use. Based on this, the Yangtze River Economic Belt (YREB) in China has been selected as an example to measure the characteristics of the spatial and temporal evolution of NCE from 2005 to 2018 by empirically testing the contributions of population urbanization, economic urbanization, and land urbanization to the NCE changes in YREB. According to the study’s findings, the carbon-neutral pressure index of the YREB increased over the study period, with an increase in NCE from 1706.50 Mt to 3106.05 Mt. The contribution of urbanization in this process increased and subsequently decreased in an inverted U pattern with a drop in the cumulative net emission of 260.32 Mt. The inflection points of the cumulative impact of urbanization on NCE in the midstream and upstream regions occurred in 2011 and 2010, respectively. Due to the high degree of urbanization and economic growth in the downstream area, the urbanization impact demonstrated a constant reduction of NCE over the research period. In terms of sub-dimensions, the population and land urbanization effects were consistently positive, while the economic urbanization affected the NCE and displayed an inverted U pattern during the study period. If the variation in regional carbon sink space is ignored, the impact of urbanization on CO2 emission reduction will be overestimated. We investigated the realization path of differentiated synergistic emission reduction strategies in the great river economic belts based on the empirical study on YREB.

Research and Analysis on the Influencing Factors of China’s Carbon Emissions Based on a Panel Quantile Model

Since the beginning of the new century, China’s carbon emissions have increased significantly, and the country has become the world’s largest carbon emitter. Therefore, determining the influencing factors of carbon emissions is an important issue for policymakers. Based on the panel data of 30 provinces and cities across the country from 2000 to 2018, this study empirically tested how per capita disposable income, industrial structure, urbanization level, average family size, and technological innovation level impacts carbon emissions at different quantile levels by using the panel quantile STIRPAT model. The results showed that per capita disposable income and industrial structure had significant promoting effects on carbon emissions, while urbanization level, average family size, and technological innovation level had significant inhibitory effects on carbon emissions. The main thing is that the emission distributions of the 10th and 90th quantiles of the independent variables were quite different, which shows that the influence of each factor on carbon emissions has obvious heterogeneity at different levels. Specifically, the impact of per capita disposable income and technological innovation level on carbon emissions in low carbon emission areas were higher than that in high carbon emission areas, and the impact of industrial structure, urbanization level, and average household size on carbon emissions in high carbon emission areas was higher. Finally, specific policy implications are provided based on these results.

Effects of Low-Carbon Visualizations in Landscape Design Based on Virtual Eye-Movement Behavior Preference

Three-dimensional geovisualization in landscape design can be used to evaluate the efforts of mitigating CO2 emissions. This study evaluated subjects’ emotional preferences for 3D landscape design through an eye movement tracking experiment. In the case that the color of the building materials was positively correlated with low carbon emissions, green, blue, and gray were typical representatives of low carbon emissions. Through the eye movement tracking experiment, subjects’ emotional preferences for different building colors were obtained. The results show that the fixation trajectory is consistent with the preset green and energy saving parameters, and the design effect of the architectural landscape can be evaluated by detecting virtual eye movement tracking. There is a coupling relationship between virtual eye movement tracking, expert interviews, and evaluation results, so that it presents a logical relationship between virtual eye movement, the color of low-carbon materials, and carbon emissions. In addition, the affective preference analysis and entropy weight method confirmed their effectiveness in the evaluation of the 3D landscape design effect, which had a positive impact on the CO2 emission reduction of the construction industry. These results will contribute to the development of 3D landscape design in the architecture industry and provide new ideas and methods for the carbon peak project.

Comparative Analysis Chinese Green Buildings’ of Input–Output Effect Based on Data Envelope Analysis

In order to measure implementation management efficiency of Chinese green buildings, the input–output index system of Chinese green buildings was constructed base on provincial-level panel data during 2017–2021.The basic model Data Envelopment Analysis (DEA) and DEA-Malmquist index method were adopted to measure and analyze the development law and time–region evolution tendency of implementation efficiency of Chinese green buildings from the static and dynamic perspectives, contributing to clarifying critical factors of restricting green building development, respectively. It was found that the comprehensive implementation management efficiency of Chinese green buildings was lower, but the development tendency was good. There was a remarkable regional and provincial difference, showing the development pattern of “east > middle > west” as a whole. Pure technical efficiency did not have a big gap, but most areas kept an invalid scale state, resulting in fluctuations of regional efficiency in varying degrees. The average annual increase of Total Factor Productivity (TFP) was 14.80%, indicating that TFP was developed well. Technical progress was considered as a decisive factor to restrain increase or decrease of TFP. As a result, to improve implementation efficiency of Chinese green buildings, it is necessary to focus on destroying the regional limitations, optimizing the input scale moderately, and paying attention to technical progress and innovation management.

How does green technology innovation affect urbanization? An empirical study from provinces of China

The construction of new-type urbanization with the theme of innovation, green, and smart development is becoming the endogenous driving force of China's economic transformation and upgrading, and green technological innovation is a key factor in cracking the problems of development motivation and environmental constraints in urbanization construction. This paper investigates the impact of green technology innovation on urbanization based on a panel dataset covering 30 provinces in China from 2005 to 2019. First, we use the entropy method and the super-efficiency DEA method to measure the level of urbanization and green technology innovation, respectively. Moreover, on this basis, we use panel regression model and FGLS model to estimate the direct impact of green technological innovation on urbanization and its three dimensions-population urbanization, industrial urbanization, and ecological urbanization. Then, the mediating effect model is used to further study the indirect impact of green technological innovation on urbanization. The results indicate that green technological innovation is the most effective way to promote the development of new urbanization currently. In addition, green technology innovation can indirectly affect urbanization through the effects of foreign capital, energy consumption and information development, while the effect of industrial structure optimization effects is not significant. Finally, some policy suggestions are discussed to better promote the development of urbanization in China.

Pyrolytic characteristics of fine materials from municipal solid waste using TG-FTIR, Py-GC/MS, and deep learning approach: Kinetics, thermodynamics, and gaseous products distribution

Fine materials (FM) from municipal solid waste (MSW) classification require disposal, and pyrolysis is a feasible method for the treatments. Hence, the behavior, kinetics, and products of FM pyrolysis were investigated in this study. A deep learning algorithm was firstly employed to predict and verify the TG data during the process of FM pyrolysis. The results showed that FM pyrolysis could be divided into drying (<138 °C), de-volatilization (138-570 °C), and decomposition stage (≥570 °C above). The de-volatilization can further be divided into stage 2 and stage 3, with values of activation energy estimated by Flynn-Wall-Ozawa and Kissinger-Akahira-Sunose methods as 123.35 and 172.95 kJ/mol, respectively. The gas products like H₂O, CO₂, CH₄, and CO, as well as functional groups like phenols and carbonyl (CO), were all detected during the process of FM pyrolysis by thermogravimetric-fourier transform infrared spectrometry at a heating rate of 10 ^°C/min. The main species detected by pyrolysis-gas chromatography-mass spectrometry analyzer included acid (41.98%) and aliphatic hydrocarbon (22.44%). Finally, the 1D-CNN-LSTM algorithm demonstrated an outstanding generalization capability to predict the relationship between FM composition and temperature, with R² reaching 93.91%. In sum, this study provided a reference for the treatment of FM from MSW classification as well as the feasibility and practicability of deep learning applied in pyrolysis.

Study on the impact of comprehensive urbanization on urban civil building CO2 emissions in China

With the rapid development of China, urbanization has become an important research topic of China's CO2 emissions. To fill the gap in considering the spatial correlation of the comprehensive urbanization that includes multi-dimensional factors on CO2 emissions from urban civil buildings (UBEC), this study constructs a comprehensive evaluation indicator of urbanization from four aspects including population, economy, society, and land urbanization by using the entropy method. The spatial spillover effect of UBEC and the impact of comprehensive urbanization on UBEC are also studied by using the spatial panel model in this paper. This study finds out that UBEC has obvious spatial spillover effects. During the early years of the study period, the eastern coastal areas had greater carbon emissions, while in recent years, they have gradually transitioned to the northwestern regions. Comprehensive urbanization has a significant promotion effect on it. And foreign direct investment and per capita energy consumption also have positive impact on UBEC. This study provides a reference for measuring the effects of urbanization on sector-specific CO2 emissions and may be useful for energy efficiency and emission abatement efforts in China.

Spatial Correlation Network and Driving Effect of Carbon Emission Intensity in China’s Construction Industry

To explore the spatial network structure characteristics and driving effects of carbon emission intensity in China’s construction industry, this paper measures the carbon emission data of China’s construction industry in various provinces from 2006 to 2017 and then combines the modified gravity model and social network analysis method to deeply analyze the spatially associated network structure characteristics and driving effects of the carbon emission intensity in China’s construction industry. The results show that the regional differences of the carbon emissions of the construction industry are significant, and the carbon emission intensity of the construction industry shows a fluctuating trend. The overall network of carbon emission intensity shows an obvious “core-edge” state, and the hierarchical network structure is gradually broken. Economically developed provinces generally play a leading role in the network and play an intermediary role to guide other provinces to develop together with them. Among the network blocks, most of the blocks play the role of “brokers”. The block with the leading economic development has a strong influence on the other blocks. The increase in network density and the decrease in network hierarchy and network efficiency will reduce the construction carbon emission intensity.

Selective Detection of Carbon Monoxide on P-Block Doped Monolayers of MoTe2

CO and CO2 are among the most commonly monitored gases. However, the currently available semiconductor sensors require heating to ∼400 °C in order to operate effectively. This increases the power demand and shortens their lifespan. Consequently, new material prospects are being investigated. The adoption of novel two-dimensional layered materials is one of the pursued solutions. MoS2 and MoTe2 sheets have already been shown sensitive to NO2 and NH3 even at room temperature. However, their response to other compounds is limited. Hence, this work investigates, by employing density functional theory (DFT) calculations, the doping of Al, Si, P, S, and Cl atoms into the Te vacancy of MoTe2, and its impact on the sensing characteristics for CO and CO2. The computations predict that P doping significantly enhances the molecule-sheet charge transfer (up to +436%) while having only a little effect on the adsorption energy (molecular dynamics show that the molecule can effectively diffuse at 300 K). On the other hand, the doping has a limited impact on the adsorption of CO2. The relative (CO/CO2) response of P-doped MoTe2 is 5.6 compared to the 1.5 predicted for the pristine sheet. Thus, the doping should allow for more selective detection of CO in CO/CO2 mixtures.

Assessment of financial development on environmental degradation in KSA: how technology effect?

The discourse on the impact of financial development and its effects on environmental quality has been an important research area in the last few decades. The objective of this research attempts to test the technology effect hypothesis on environmental mitigation in the case of Saudi Arabia (KSA) over the period 1970-2016 and the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) with the Autoregressive Distributed Lag (ARDL) model used for empirical inquest. Unlike others, we insert additional variables such as technology, human capital, and the technology effects of financial development into the carbon dioxide emission model. We used the Ng-Perron unit root test to examine the stationary properties of the variables. Similarly, to examine the presence of the cointegration relationship between carbon dioxide emissions and its determinants, the Bound cointegration with multiple structural breaks approach is applied. First, The empirical findings show that financial development and technology have a negative and significant impact on environmental degradation. Second, the technology effects of financial development have an unfortunate effect on environmental mitigation. Finally, lower environmental mitigation is associated with a deepening in total population and affluence. Moreover, findings from the pairwise Granger causality test point that there is no causality running from both financial development and technology to the effect of technology among KSA. On the opposite, we looked at economic growth Granger, cause environmental quality. In addition, a unidirectional causality was seen running from environmental quality to financial development. Similarly, the relationship between affluence and financial development in KSA is unidirectional. Thus, various policy implications should be proposed to policymakers as enhancing the expansion of technology, especially in the industrial sector by incorporating renewable energy consumption to upgrade environmental quality.

A building carbon emission prediction model by PSO-SVR method under multi-criteria evaluation

Building carbon emission prediction plays an irreplaceable role in low-carbon economy development, public health protection and environmental sustainability. It is significant to identify influential factors mainly contributed to building emission and predict emission accurately in order to harness the growth from the source. In this paper, 11 influencing factors of building carbon emission are identified and a support vector regression (SVR) prediction model is proposed to forecast building carbon emission considering improvement the prediction accuracy, generalization, and robustness. In the SVR model, parameters are optimized by particle swarm optimization (PSO) algorithm with the aim to improve performance. Cases in Shanghai’s building sector are adopted to demonstrate practical applications of the proposed PSO-SVR prediction model. The results indicate that the presented prediction system has an outstanding performance in forecasting building carbon emission under multi-criteria evaluation. Furthermore, compared to the results from other four prediction models (e.g., linear regression, decision tree), it is shown that PSO-SVR model can achieve higher accuracy (e.g., improvement average of 1.01% R2 under training subset), better generalization (e.g., improvement average of 19.89% R2 under testing subset), and better robustness (e.g., improvement average of 18.93% R2 under different levels of noise intensity).

Monitoring the spatial spillover effects of urbanization on water, built-up land and ecological footprints in sub-Saharan Africa

Despite the considerable attention given to the environmental implications of urbanization, evidence on the role of urbanization in the ongoing biodiversity loss lacks from an African perspective. In this perspective, we explore the threats urbanization poses on biodiversity with a focus on water, land, and the overall ecosystems across 28 sub-Saharan African (SSA) countries over the period 2000-2017. Specifically, we employ water, land, and ecological footprints as metrics to characterize human-induced pressures on biodiversity. Unlike previous studies, we demonstrate that urbanization displays heterogeneous effects on footprint indicators, and the consideration of a single footprint item may underestimate the impact of urbanization on biodiversity loss. The results show that urbanization contributes to the expansion of human-induced pressures on water and built-up landscape. Furthermore, there is evidence that being surrounded by highly urbanized countries increases the per capita water and built-up land footprints. These spatial spillover effects of urbanization should be considered when establishing pathways to conservation policies.

Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics

The dramatic growth in the number of buildings worldwide has led to an increase interest in predicting energy consumption, especially for the case of residential buildings. As the heating and cooling system highly affect the operation cost of buildings; it is worth investigating the development of models to predict the heating and cooling loads of buildings. In contrast to the majority of the existing related studies, which are based on historical energy consumption data, this study considers building characteristics, such as area and floor height, to develop prediction models of heating and cooling loads. In particular, this study proposes deep neural networks models based on several hyper-parameters: the number of hidden layers, the number of neurons in each layer, and the learning algorithm. The tuned models are constructed using a dataset generated with the Integrated Environmental Solutions Virtual Environment (IESVE) simulation software for the city of Buraydah city, the capital of the Qassim region in Saudi Arabia. The Qassim region was selected because of its harsh arid climate of extremely cold winters and hot summers, which means that lot of energy is used up for cooling and heating of residential buildings. Through model tuning, optimal parameters of deep learning models are determined using the following performance measures: Mean Square Error (MSE), Root Mean Square Error (RMSE), Regression (R) values, and coefficient of determination (R2). The results obtained with the five-layer deep neural network model, with 20 neurons in each layer and the Levenberg–Marquardt algorithm, outperformed the results of the other models with a lower number of layers. This model achieved MSE of 0.0075, RMSE 0.087, R and R2 both as high as 0.99 in predicting the heating load and MSE of 0.245, RMSE of 0.495, R and R2 both as high as 0.99 in predicting the cooling load. As the developed prediction models were based on buildings characteristics, the outcomes of the research may be relevant to architects at the pre-design stage of heating and cooling energy-efficient buildings.

Examining the Impacts of Economic, Social, and Environmental Factors on the Relationship between Urbanization and CO2 Emissions

This study examines the relationship between urbanization, economic growth, industrial transformation, technological change, public services, demographical change, urban and natural environmental changes, and carbon emissions using a dataset of 182 prefecture-level cities in China between 2001 and 2010. Specifically, this paper differs from previous studies in two aspects. First, the extant literature has focused on how economic processes accompanying rapid urbanization affect carbon emissions in urban areas but gives little attention to the other dimensions of urbanization, including social and environmental changes, which may have important effects on carbon emissions. We assessed the effects of 17 key processes accompanying urbanization in a full range of economic, social, and environmental dimensions on carbon dioxide emissions in urban areas. The results showed that social processes accompanied with rapid urbanization had distinct effects on carbon emissions, compared to economic and environmental processes. Specifically, improvement in public services, indicated by education and cultural developments, reduces the increase in carbon emissions during urbanization, while economic growth and urban construction reinforces the growth in carbon emissions. Second, we examined the impact of various urbanization processes on carbon dioxide emissions using a unique dataset of 182 prefecture-level cities that covers a wide span of regions in China. The results of our analyses on the city level have important implications for the formulation of comprehensive policies aimed at reducing carbon dioxide emission in urban areas, focusing on different urbanization processes in economic, social, and environmental phases.

The impact of economic growth, energy consumption, trade openness, and financial development on carbon emissions: empirical evidence from Malaysia

Globally, the rising concentration of anthropogenic greenhouse gases emission in the atmosphere is extremely detrimental to the environment. The high concentration among all greenhouse gases is carbon dioxide emission. Therefore, this study explores the linkages between energy consumption, trade openness, globalization, urbanization, and carbon dioxide emission for Malaysia over the spans from 1978 to 2018. ARDL bound testing model was employed to investigate involvement in the elevation of carbon dioxide emissions in the economy. The study illustrates that a 1% growth in energy consumption, trade openness, and urbanization will deteriorate the environment by 0.18%, 0.03%, and 2.51% respectively. Further, variance decomposition analysis predicts that all the determinants in the study have significantly caused carbon dioxide emission in Malaysia. The paper presents scientific support for further studies and argues for the use of innovation shocks as a policy instrument for a prosperous future by formulating more successful environmental policies.

Influence of Population Density on CO2 Emissions Eliminating the Influence of Climate

More than 50% of the world’s population lives in cities. Its buildings consume more than a third of the energy and generate 40% of the emissions. This makes cities in general and their buildings in particular priority points of attention for policymakers and utilities. This paper uses population density as a variable to know its influence on energy consumption and emissions produced in buildings. Furthermore, to show its effect more clearly, the influence of the climate was eliminated. The usual energy consumption in buildings is thermal and electrical. The study was carried out at the city level, both per inhabitant and per household. The area actually occupied by the city was considered. The proposed method was applied to the case of Spanish cities with more than 50,000 inhabitants. The results show that the higher the population density, the higher the energy consumption per inhabitant and household in buildings. The consumption of thermal energy is elastic, while that of electrical energy is inelastic, varying more than 100% between extreme groups. Regarding CO2 emissions, the higher the population density, the higher the emissions. Emissions of electrical origin barely vary by 2% and are greater than those of thermal origin. In addition, the proportion of emissions of electrical origin, with respect to the total, decreases with increasing population density from 74% to 55%. This research aims to help policymakers and utilities to take the appropriate measures that favor the use of renewable energies and reduce CO2 emissions.

Impacts of high-speed rail on urban smog pollution in China: A spatial difference-in-difference approach

Smog pollution poses a severe threat to residents' health and economic development in China. High-speed rail (HSR) is a new and efficient infrastructure that is expected to provide economic and environmental benefits. Based on the STIRPAT model and the environment Kuznets curve (EKC) hypothesis, this study employs a spatial difference-in-difference approach using 284 prefecture-level cities' panel data from 2007 to 2016 to explore the impacts of HSR on urban smog pollution. The results demonstrate that urban smog pollution shows strong spatial correlations and that HSR can significantly reduce smog pollution. Causal mediation analysis is used to test two mechanisms related to HSR: sector structure upgrading, which can reduce smog pollution, and real estate market development, which tends to increase smog pollution. After controlling for the two opposite mechanisms, HSR is proven to have positive environmental benefits. Besides HSR, the impacts of per capita GDP and population on smog pollution are further discussed. The relationship between per capita GDP and urban smog pollution follows an N-shaped curve, and smog is proved to reduce to a certain extent as per capita GDP increases. The relationship between population and smog pollution shows a U-shaped curve, provided with a new interpretation relating to economies of scale. The findings have implications for policy-making, as they enrich the EKC hypothesis and provide evidence for the environmental benefits of HSR.