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Carbon emission trading scheme (ETS) policy is a quota-based market-motivated environmental regulation policy that by transforming environmental responsibility of enterprises into self-consciousness behavior induces green innovation behavior in enterprises and influences the level of regional green innovation. Based on panel data on green patents in 30 Chinese provinces from 2010 to 2019, the paper uses six Chinese pilot provinces for ETS as the treatment group to investigate the induced effects of Chinese ETS policy on the quantity and quality of green technology innovation using difference in difference model and analyzes the effects of Chinese ETS policy on green technology innovation in different pilot regions using a synthetic control method. The findings indicate that (1) compared with non-pilot areas, the carbon emission trading policies in pilot areas can induce green technology innovation activities. (2) In all pilot regions, ETS policies significantly promoted the quantity and quality of green technology innovation, but the quantity promotion effect was significantly better than the quality promotion effect, and the low-quality promotion effect was better than the high-quality promotion effect. (3) In terms of individual pilot region, ETS policies in Guangdong, Hubei and Tianjin significantly promoted the quantity of green technology innovation and low-quality green technology innovation, but significantly inhibited or suppressed high-quality green technology innovation in a short term. This paper not only enriches the research on environmental equity trading policies and green technology innovation theoretically, but also provides empirical evidence for the effectiveness of carbon emission trading schemes pilots program in China. [ABSTRACT FROM AUTHOR]

As Chi na's shipping industry continues to develop, ship emissions have become a significant source of pollutants. Consequently, it has become imperative to comprehend accurately the nature and attributes of ship pollutant emissions and understand their causation and effect as a crucial aspect of pollution control and legislation. This paper employs high-precision automatic identification system (AIS) dynamic and static data, along with pollutant emission parameters, to estimate the pollutant emissions from a ship's main engine, auxiliary engine, and boiler using a dynamic approach. Additionally, the study considers the sailing state and trajectory of the vessel and analyzes the characteristics of ship carbon emissions. Taking Tianjin Port as an example, this study conducts a multi-dimensional analysis of pollutant emissions to gain insight into the causation and effect of pollutants based on the collected big AIS data. The results show that the pollutant emissions in this region are mainly concentrated in the vicinity of Tianjin Port land port area, Dagusha Channel, and the Main Shipping Channel of Tianjin Xingang Fairway. Carbon emissions peak in September and are lower in June and December. Through accurate analysis of pollutant emission sources and emission characteristics in the region, this paper establishes the regular relationship between pollutant emissions and possible influencing factors and provides data support for China to formulate accurate pollutant emission reduction policies and regulate ship construction technology and carbon trading. [ABSTRACT FROM AUTHOR]

CO2 is the main greenhouse gas. Urban spatial development, land use, and so on may be affected by CO2 and climate change. The main questions studied in this paper are as follows: What are the drivers of CO2 emissions of expanding megacities? How can they be analyzed from different perspectives? Do the results differ for megacities at different stages of development? Based on the XGBoost model, this paper explored the complex factors affecting CO2 emissions by using data of four Chinese megacities, Beijing, Tianjin, Shanghai, and Chongqing, from 2003 to 2017. The main findings are as follows: The XGBoost model has better applicability and accuracy in predicting carbon emissions of expanding megacities, with root mean square error (RMSE) as low as 0.036. Under the synergistic effect of multiple factors, population, land size, and gross domestic product are still the primary driving forces of CO2 emissions. Population density and population become more important in the single-factor analysis. The key drivers of CO2 emissions in megacities at respective developmental stages are different. This paper provides methods and tools for accurately predicting CO2 emissions and measuring the critical drivers. Furthermore, it could provide decision support for megacities to make targeted carbon-emission-reduction strategies based on their own developmental stages. [ABSTRACT FROM AUTHOR]

The Chinese government aims to balance employment stabilization and emissions reduction when it initiates an emissions trading scheme (ETS) to control carbon emissions. However, the policymakers should not neglect the role of imperfectly competitive market structure and market power from incumbent firms. In view of this, classical framework of Berman and Bui's (2001) is extended in this paper to clarify the theoretical mechanism of employment effect of the ETS considering firm-level market power. In addition, based on the firm-level panel data from 2008 to 2019 of China, the employment effect of each pilot ETS is measured with the method of difference-in-difference-in-differences. Then a moderating effect model is established to verify the role of firm-level market power on the heterogeneous employment effect among the pilot ETSs. Moreover, the employment effect is further decomposed to evaluate the influential mechanism from the perspective of firms' strategic behavior. It is found that there is negative employment effect of China's ETS policy, due to the significant moderating role of firm-level market power. This decrease in labor demand is mainly dominated by a negative output effect rather than a positive substitution effect because strategic allowance sellers with stronger market power tend to hire fewer employees to reduce carbon emissions by controlling production scale. However, the heterogeneity of policy effects resulted by market structure is also identified in different pilot regions. Employment dividend has been found in Beijing and Tianjin where strategic allowance buyers manipulate the carbon market and hire more employees to consolidate their market power. Therefore, policymakers should improve the design of the ETS to overcome the adverse employment effect duo to the region-specific market structure. [ABSTRACT FROM AUTHOR]

The goal of carbon peak and carbon neutralization puts forward higher requirements for the energy and electricity industry development in China. Jing-jin-ji region (short for Beijing, Tianjin and Hebei Provinces) as an important economic growth pole in China, its electric power industry realizing low-carbon transformation can play a good exemplary role nationwide. To clarify the key factors affecting carbon dioxide emission from electric power industry and analyze the relationship between emission and regional economic growth, the paper established decoupling effort model based on LMDI two phases decomposition model and Tapio decoupling index. Firstly, we collected the data of Jing-jin-ji region from 2000 to 2017 and conducted factors decomposition. The results show that: among the ten key factors affecting electric power industry CO2 emission, power generation and consumption ratio, fossil energy consumption coefficient and industrial structure have the most significant effect on CO2 emission reduction, with contribution rates of − 23.27, − 16.50 and − 11.91%, respectively. Per capita GDP and total population are the main positive driving factors, with contribution rates of 132.20% and 23.38%, respectively. Then from the perspective of decoupling relationship, the electric power industry in Jing-jin-ji region has entered a weak decoupling state since the Eleventh Five Year Plan with the decoupling index declined from 0.85 in 2004 to 0.38 in 2017. Excluding the influence of economic development, there are six driving factors to realize the weak decoupling, including (1) Power generation and consumption ratio effect, (2) Industrial structure effect, (3) Industrial power consumption intensity effect, (4) Fossil energy consumption coefficient, (5) Thermal power proportion effect and (6) Fossil energy power generation structure effect, in which the decoupling degree decreases in turn. The total population effect, transmission and distribution loss effect and resident power consumption intensity effect have not achieved decoupling status. At last, based on the model calculation results, we put forward suggestions to promote the low-carbon development of power industry in Jing-jin-ji region, for example, accelerate industrial structure upgrading and the adjustment of power generation structure; Strengthen electricity demand side management; Promote the construction of ultra-high voltage and smart grid as a whole. [ABSTRACT FROM AUTHOR]

Electric heating is helpful in resolving air pollution, and also utilizes renewable energy power. However, the environmental benefits of electric heating are restrained by regional resources endowment and regional electrical energy structure. In the context of the active promotion of electric heating in Beijing—Tianjin–Hebei, the paper studied the development potential and limits of wind power electric heating under the constraints of environmental effects to provide reference for the orderly development of electric heating. A model to predict the capacity of the installed wind power improved by the fruit fly optimized ELM (Extreme Learning Machine) algorithm and an electric heating environment effect calculation model are constructed. The optimal value and critical value of the newly added heating area in Beijing–Tianjin–Hebei are obtained through calculation under four scenarios of wind abandonment rate. As the wind abandonment rate increases from 4–10% (with interval of 2%), the optimal value of the newly added electric heating area in Beijing—Tianjin–Hebei will be 19.15, 29.47, 36.26, and 47.90 million m2 by the year 2030. Additionally, as the utilization rate of thermal power exceeds 41.16%, electric heating will produce more carbon emissions compared to coal‐fired heating. Therefore, if we want to develop electric heating on a large scale, we need to control the proportion of thermal power in the power supply structure and increase the proportion of new energy. This study can be used as theoretical reference to the development programme of electric energy substitution in the future. © 2021 Society of Chemical Industry and John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper explores the spatial and temporal variability and changes in characteristics of the economic efficiency of urban construction land (EEUCL) in 14 administrative regions in the Beijing–Tianjin–Hebei (BTH) urban agglomeration from 2002 to 2019, based on the super-efficiency SBM-DEA model and Malmquist index, using carbon emissions as the non-desired output. On this basis, the Tobit model is used to study the role of nine external environmental factors, such as the level of socioeconomic development and industrial structure, in influencing the EEUCL. The results indicate that (1) carbon emissions from urban construction land in Beijing, Tianjin, and Hebei have all reached their peak, and the growth rate has slowed down significantly in the past two years, although there has been a slight rebound; (2) from the 10th Five-Year Plan period to the 13th Five-Year Plan period, Beijing has maintained a high level of EEUCL and the trend is slowly increasing. However, the EEUCL values of Tianjin and Hebei are gradually declining; (3) the socio-economic development level, industrial structure, and green economy development level have a significant positive impact on the EEUCL in the BTH region, while the urban scale, scientific and technical support intensity, outward opening level and population density have significant negative effects. [ABSTRACT FROM AUTHOR]

According to the proposal of Kyoto protocol, carbon dioxide emission rights are traded as a commodity, and carbon emission trading market emerges as the times require. As the world's largest carbon emitter, China has established eight pilot markets for carbon emission trading. Selecting the closing price of eight carbon trading markets from the establishment to June 23, 2020, this paper analyzes the daily, weekly, and monthly return series data, using the first-order autoregressive process to adjust the daily income series to eliminate the weak trading market effect and then comprehensively uses the traditional variance ratio test and multiple variance ratio test to analyze the weak-form market efficiency of the eight carbon trading markets. The empirical results show that most of the carbon trading markets are non-weak-form market efficiency, and only Tianjin, Shanghai, and Hubei markets are weak-form market efficiency under the daily trading data. However, with the increase of carbon holding period, the weak-form market efficiency continues to strengthen. It shows that liquidity, quantity, and information transparency are important factors that affect the market efficiency. [ABSTRACT FROM AUTHOR]

Improving carbon emission efficiency (CEE) would promote the development of the green and low-carbon economy in the Beijing-Tianjin-Hebei (BTH) region, China. This paper uses the EBM model of unexpected output to measure the city-level CEE of the BTH region from 2007 to 2016. The spatial distribution characteristics and evolution law of CEE are analyzed with respect to overall and local aspects, and the spatial quantile regression model is used to verify the influencing factors of CEE. The main findings are as follows: (1) The carbon emission in the BTH region is considered to be of medium efficiency, and there are eight cities within the region at middle- and high-efficiency levels. The overall efficiency values show a downward trend. Beijing, Cangzhou, Baoding have high-CEE values, whereas Handan, Tangshan, and Zhangjiakou have low-CEE values. (2) The CEE values for BTH show significant spatial agglomeration characteristics at both the global and local levels. The "H–H" agglomeration areas are primarily distributed in the central region, and the "L-L" agglomeration areas are chiefly distributed in the southern and northern regions. The spatial pattern change is generally stable. (3) The selected factors, URB, PGDP, DS, ISG, FDI, and TEL, have different regression coefficients on CEE at different quantiles. [ABSTRACT FROM AUTHOR]

Global warming is currently an area of concern. Human activities are the leading cause of urban greenhouse gas intensification. Inversing the spatial distribution of carbon emissions at microscopic scales such as communities or controlling detailed planning plots can capture the critical emission areas of carbon emissions, thus providing scientific guidance for intracity low-carbon development planning. Using the Sino—Singapore Tianjin Eco-city as an example, this paper uses night-light images and statistical yearbooks to perform linear fitting within the Beijing—Tianjin—Hebei city-county region and then uses fine-scale data such as points of interest, road networks, and mobile signaling data to construct spatial characteristic indicators of carbon emissions distribution and assign weights to each indicator through the analytic hierarchy process. As a result, the spatial distribution of carbon emissions based on detailed control planning plots is calculated. The results show that among the selected indicators, the population distribution significantly influences carbon emissions, with a weight of 0.384. The spatial distribution of carbon emissions is relatively distinctive. The primary carbon emissions are from the Sino—Singapore Cooperation Zone due to its rapid urban construction and development. In contrast, carbon emissions from other areas are sparse, as there is mostly unused land under construction. [ABSTRACT FROM AUTHOR]

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With the rapid development of China's economy, the contradiction of the mismatch between municipal solid waste disposal capacity and its growth has become increasingly prominent, and the need to develop alternative energy sources has become increasingly urgent. Municipal solid waste incineration plays an important role in waste management. In this study, the Grey Relation Analysis (GRA) and Long Short Term Memory (LSTM) prediction model is constructed to achieve effective prediction of municipal solid waste generation by taking 11 influencing factors of municipal solid waste generation as input indicators in Tianjin. Meanwhile, this study predicts the power generation from municipal solid waste incineration and carbon emissions in Tianjin based on the prediction results. The experimental results show that the mean absolute percentage error (MAPE) value of the combined GRA-LSTM model established in this study is 6.759 %, and this model outperforms the other five structural methods in terms of prediction performance. By 2025, the amount of MSW in Tianjin will reach 3.74 million metric tons, the power generation through incineration will be 20.02–39.47 GWh, and the CO 2 emission through incineration will be about 1.14 million metric tons. Finally, to cope with the growth of municipal solid waste generation and unbalanced power supply structure in the country's mega-cities, this paper puts forward relevant suggestions for achieving sustainable urban development. [ABSTRACT FROM AUTHOR]

The coordination and balance between agricultural development (AD) and agricultural carbon emissions (ACE) is one of the most important ways to boost the high-quality development of agriculture in the Beijing-Tianjin-Hebei region. Taking 13 prefecture-level cities in the Beijing-Tianjin-Hebei region as the research object, this study uses the comprehensive evaluation method and Tapio decoupling model to analyze the decoupling effect between the AD level and the Agricultural Carbon emissions intensity (ACEI) from 2000 to 2020, based on the assessment of AD status and the calculation of Agricultural Carbon emissions quantities (ACEQ) and ACEI. It found that: (1) From 2000 to 2020, the AD in the Beijing-Tianjin-Hebei region generally showcased a gradual increase trend, and demonstrated a basic feature that AD in the northern areas was higher than that in the southern ones. (2) From 2000 to 2020, the ACEQ in the Beijing-Tianjin-Hebei region showed a trend of first increasing and then decreasing, with a spatial distribution feature that the ACEQ in the southern cities was higher than that in the northern ones. Regarding the source of ACE, the livestock and poultry farming took the highest proportion. ACEI was decreasing year by year, higher in the southern areas than in the northern ones. (3) The main types of decoupling in the Beijing-Tianjin-Hebei region were strong decoupling, recession decoupling, strong negative decoupling, weak negative decoupling, recession coupling, and expansion negative coupling. The decoupling relationship between AD and ACEI were in dynamic change, but the change trend of the decoupling relationship was optimistic. The results of this study deliver certain deployable practice value for improving the sustainability of regional agricultural green development and ecological environmental protection. [ABSTRACT FROM AUTHOR]

Based on the whole life cycle theory, the carbon effect of three different sizes of high-standard basic farmland construction projects is measured and analysed. The results show that the carbon emissions generated during the construction of high-standard basic farmland projects and the carbon absorption capacity at the later stage are different for projects of different sizes. The carbon emissions of different scales of high-standard basic farmland projects will increase during the construction stage. The results of carbon effect changes in the later operation and management stage show that the high-standard basic farmland construction projects will help reduce the carbon emissions of the field ecosystem where the farmland is located and increase its carbon sink capacity after the completion of construction, which is more obvious in larger projects. The emission reduction and carbon sequestration capacity of the farmland after remediation are improved to different degrees, which is more conducive to the ecological development of agricultural production and ecological environmental protection in the relevant areas. The study contributes to the green development of farmland, which is of some significance for the sustainable development of agriculture in Tianjin and the whole country. [ABSTRACT FROM AUTHOR]

Pairs trading is one of the mainstream statistical arbitrage strategies in current financial market practice that can seize possible arbitrage opportunities caused by mispricing between similar assets. Based on the theory of statistical arbitrage and the similar attributes of carbon emission allowances traded in different carbon markets, this paper studies the applicability of the pairs trading strategy among different pilot markets in China. The novelty and possible contribution of this paper are as follows. Firstly, this paper extends the statistical arbitrage-based pairs trading framework to the analysis of carbon market transactions in China, which broadens the understanding of the correlation between China's carbon markets. Secondly, this study explores the arbitrage opportunities among China's regional carbon markets that have not been revealed by previous studies. Thirdly, based on the pairs trading analytical model adopted in this study, this paper further investigates the operation characteristics of China's carbon markets and the interaction between markets, as the sensitivity of strategy performance to the key parameters is also discussed. The results show that arbitrage based on pairs trading is profitable. In the baseline case, the annualized arbitrage returns between the Shanghai emission allowance and Beijing emission allowance is about 9.11%, the highest among all pairs. However, there is almost no pairing relationship between the Tianjin carbon market and other markets. By discussing the time distribution of arbitrage signals generated by the pairs trading strategy, it is found that most signals are sent out during the inactive trading period in a compliance cycle. The conclusions provide valuable insights for formulating relevant carbon market regulatory policies and investors' participation in arbitrage activities in China's carbon trading markets. • The application of the pairs trading strategy is extended to the Chinese carbon markets. • Based on relative value arbitrage theory, arbitrage opportunities between markets are examined. • Pairs trading strategy can also yield sustainable and distinctive returns in the Chinese carbon markets. • Arbitrage trading between the Shanghai and Beijing markets may yield promising annualized returns with about 9.11%. [ABSTRACT FROM AUTHOR]

Beijing-Tianjin-Hebei metropolitan area is a significant carbon emission center. The region's early achievement of peak carbon targets is critical to the nation's achievement of peak carbon targets. In this paper, it is proposed to use different orders of grey models to classify into three scenarios. Based on three scenarios, the grey multivariate convolutional model with new information priority accumulation is adopted to predict carbon emissions in the Beijing-Tianjin-Hebei region and select the scenario suitable for local development. The results show that: (1) The Beijing region has already achieved peak carbon, the Tianjin region may not reach its peak carbon target by 2030, and the Hebei region is expected to reach its peak carbon target by 2030. (2) The high rate of carbon emission reduction scenario will greatly improve the air quality of Beijing. The low-speed growth carbon emission scenario is more in line with the future development of Tianjin city. The low-rate carbon reduction scenario is more in line with the synergistic governance of pollution reduction and carbon reduction in Hebei Province. (3) Beijing's population policy in the most recent years has been conducive to improving the local environment. Tianjin's medium-term population policy is more in line with the local area. Hebei's medium-term industrial structure reform is favorable to local development. • Grey correlation analysis is used to analyze the factors affected carbon emissions in a region. • The factors with high correlation are predicted by using grey prediction models of different orders. • The high rate of carbon emission reduction scenario will greatly improve the air quality of Beijing. [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]

A reasonable allocation of carbon emission quotas is essential to improving the national unified carbon trading market. A bankruptcy game model is developed for allocating carbon emission quotas to Chinese provinces based on bankruptcy theory and the Nash bargaining game. The model takes into account the disparities that exist among these provinces. The allocation scheme under the carbon peak target can be obtained by using the projection pursuit model and the subject satisfaction function. The findings reveal that certain eastern provinces bear a greater responsibility in terms of historical carbon emissions. Furthermore, the rampant transfer of carbon emissions from energy-rich to energy-demanding regions has exacerbated the imbalance in regional development. Interestingly, the carbon emission demands are fulfilled in some provinces situated in the coastal regions of the southwest and southeast, such as Beijing, Tianjin, and Fujian. However, certain provinces in the northeast express the lowest levels of satisfaction with their carbon quota allocation, necessitating a greater burden of carbon emission reduction in the future. The research offers significant implications for regional development and carbon reduction efforts. • A bankruptcy game model is developed for allocating carbon emission quotas. • The model takes into account disparities between the provinces. • The bankruptcy theory and Nash bargaining game are used in this paper. [ABSTRACT FROM AUTHOR]

Owing to the complexity of metros, the energy consumption characteristics of metro systems exhibit variability and the energy‐saving management of the systems encounters challenges. To encapsulate the essential characteristics of energy usage and to objectively assess the energy performance of metro systems, this study presents a generalized framework and applies it to a case study conducted in Tianjin. The study also employs correlation analysis to investigate the applicability of the indicators relevant to ridership. The results indicate that the monthly traction electricity consumption exhibits slight variation, while station electricity usage demonstrates substantial fluctuation with seasonal changes. For Tianjin Metro, the passenger factor hardly shows any effect on the electricity use of metro lines. The median value of traction electricity use is approximately 2.0 kWh/(car‐km) and that of the average annual station electricity use of underground lines ranges from 95 to 155 kWh/m2. The emission from the traction sector is 12.2 kgCO2/(vehicle‐km) and from the station sector is 118.6 kgCO2/m2. The study also identifies the energy‐intensive lines of the Tianjin Metro and compares the energy utilization among various global metro systems. The authors hope that this study can help shed light on the assessment of the energy status of metro systems and serve as a source of information for other City‐Metros to implement energy‐saving management. [ABSTRACT FROM AUTHOR]

With regard to the aims of achieving the "Dual Carbon" goal and addressing the significant greenhouse gas emissions caused by urban expansion, there has been a growing emphasis on spatial research and the prediction of urban carbon emissions. This article examines land use data from 2000 to 2020 and combines Grid and the PLUS model to predict carbon emissions in 2030 through a multi-scenario simulation. The research findings indicate the following: (1) Between 2000 and 2020, construction land increased by 95.83%, with carbon emissions also increasing. (2) By 2030, for the NDS (natural development scenario), carbon emissions are expected to peak at 6012.87 × 104 t. Regarding the ratio obtained through the EDS (economic development scenario), construction land is projected to grow to 3990.72 km2, with expected emissions of 6863.29 × 104 t. For the LCS (low-carbon scenario), the "carbon peak" is expected to be reached before 2030. (3) The intensity of carbon emissions decreases as the city size increases. (4) The shift of the center of carbon emission intensity and the center of construction land all indicate movement towards the southeast. Studying the trends of regional land use change and the patterns of land use carbon emissions is beneficial for optimizing the land use structure, thereby enabling us to achieve low-carbon emission reductions and sustainable urban development. [ABSTRACT FROM AUTHOR]

The carbon peaking and carbon neutrality strategy have become China's national strategy. Given the significant regional differences in China, the Chinese government has proposed "one policy for one province" guiding principle for carbon emission reduction. This study takes Tianjin as a representative of "a province-level city with high degree of industrialization and prominent emerging industries", and Gansu Province a representative of "a province with a low degree of industrialization but the potential for green energy". Using the integrated modeling frameworkthis study conducts a comparative analysis of the hidden flows generated by inter-provincial inward and outward transfers and extracts a feasible model for carbon neutrality transformation. The study also focuses on carbon emission, GDP changes, carbon hidden flow change in trade and carbon emission reduction path. By comparing the results, this study identifies the mechanism of the industrial spillover effect and carbon emission spillover effect between the two regions. Then it concludes the "cross-regional transfer mode of high-carbon industries" of industrial carbon neutral transformation: more carbon emission allowances are allocated to developing regions represented by Gansu Province, implement regionally differentiated carbon emission reduction constraint policies, and achieve industrial spatial transfer through more inter-provincial transfer. The paper found that in implementing a regionally differentiated carbon emission reduction constraint policy (DE scenario), the transfer of high-carbon industries can achieve the "superposition effect" through inter-provincial transfer. Accordingly, three carbon policy mechanisms for cross-regional transfer of high-carbon industries are summarized. [Display omitted] [ABSTRACT FROM AUTHOR]

This study addresses the comparative carbon emissions of different transportation modes within a unified evaluation framework, focusing on their carbon footprints from inception to disposal. Specifically, the entire life cycle carbon emissions of High-Speed Rail (HSR), battery electric vehicles, conventional internal combustion engine vehicles, battery electric buses, and conventional internal combustion engine buses are analyzed. The life cycle is segmented into vehicle manufacturing, fuel or electricity production, operational, and dismantlingrecycling stages. This analysis is applied to the Beijing-Tianjin intercity transportation system to explore emission reduction strategies. Results indicate that HSR demonstrates significant carbon emission reduction, with an intensity of only 24 %-32 % compared to private vehicles and 47 %-89 % compared to buses. Notably, HSR travel for Beijing-Tianjin intercity emits only 24 % of private vehicle emissions, demonstrating the emission reduction benefits of transportation structure optimization. Additionally, predictive modeling reveals the potential for carbon emission reduction through energy structure optimization, providing a guideline for the development of effective transportation management systems. [ABSTRACT FROM AUTHOR]

As a clean form of energy utilization, wind power is important for alleviating climate change. Although no direct carbon emissions occur in wind power generation, there exist upstream carbon emissions from manufacturing and installation, which have indirect effects on both the locations of wind farms and areas involved in upstream production and manufacturing. In this paper, based on Input–Output based Life Cycle Analysis (IO-LCA), we explored the lifetime carbon emissions of 378 wind farms in China that were still in operation in 2015. The regional distributions of carbon emissions from wind farms during the whole lifetime were depicted. The embodied carbon emission transfers from the location of the wind farm operation to upstream turbine manufacturing regions were traced. The net emission reduction benefits among regions were also calculated. Results show that carbon emissions mainly distribute in Liaoning, Inner Mongolia, and Tianjin in the turbine manufacturing stage, with a total amount of 3.36 MT. Inner Mongolia contributes the largest carbon emissions (5.94 MT) in the farm construction stage. Inner Mongolia has transferred about 0.99 MT carbon emissions to itself and has the largest net emission reduction. Recognizing the carbon emission transfer of wind farms and dividing the carbon emission reduction responsibilities among regions may shed light on supply chain carbon emission reduction and provincial carbon quota allocation. [ABSTRACT FROM AUTHOR]

Emission Trading Scheme (ETS) is one of the most important instruments introduced to meet carbon neutral targets internationally. To reduce carbon emissions, China has established eight regional ETS since 2013 to reduce carbon emissions. However, significant heterogeneities exist across the eight pilots, resulting in distinctively different carbon price movements. Traded as a financial asset, carbon allowance is vulnerable to internal and external shocks. Though carbon prices have been fluctuating fiercely since the inception of the carbon market, little is known about potential carbon price bubbles in any of these pilots. To this end, this paper resorts to the generalized sup Augmented Dickey-Fuller (GSADF) test, which allows the detection of multiple bubbles, as well as the date stamping of bubbles in carbon prices. For the first time, price bubbles are found in the Chinese carbon markets. The empirical results find three bubbles in Guangdong pilot, two bubbles in Tianjin pilot, and one bubble in Hubei pilot. These explosive episodes are closely related to immature market mechanisms and policy implementations. The results provide insightful implications for the upcoming unified national carbon market. • This paper detects the carbon price bubbles in Chinese markets. • Multiple bubbles exist in different pilots. • Not all pilots encounter bubbles. [ABSTRACT FROM AUTHOR]

Land-use change and the associated influences on the carbon metabolism system should not be overlooked in the context of rapid urbanization. By combing carbon emissions/sequestrations accounting and ecological network analysis, this paper quantitatively described the general situation and spatial distribution evolution of carbon metabolism in the Beijing-Tianjin-Hebei (BTH) urban agglomeration from 2015 to 2019, investigated carbon flows induced by land-use change, and revealed the ecological relationships between the components of the system. The spatial distribution of carbon emissions showed a trend of high in the southeast and low in the northwest, while carbon sequestrations were the opposite. At the early stage of regional coordinate development, the migration of enterprises with heavy energy consumption and emission and expansion of the transportation network has promoted the economic development in the surrounding area of Beijing, but also lead to the carbon emission transfer and the severe imbalance of carbon metabolism. Despite that a slight improvement can be found in the carbon metabolism system of Beijing and Tianjin, Hebei province still faces intensely competitive ecological relationships between industrial land and other land use, and has a relatively low mutualism index. It is suggested that integrated land use planning, strict cultivated land protection policies, and proper ecological compensation mechanism are the key measures to facilitate the sustainable development of the BTH urban agglomeration. • A temporal-spatial evolution of carbon metabolism in the BTH urban agglomeration is presented. • The impacts of land use change on urban carbon metabolism system are investigated. • Ecological relationships between land-use changes are explored based on the ENA method. • Industrial transfer and expanded transportation exacerbate carbon metabolism imbalance. [ABSTRACT FROM AUTHOR]

In China, road traffic carbon emissions and their share in total carbon emissions have significantly increased. In the context of double carbon, the Beijing-Tianjin-Hebei region, the largest urban agglomeration in northern China, is receiving more and more attention. Due to the unbalanced development in Beijing-Tianjin-Hebei urban agglomeration, this thesis presents three computational models to calculate road traffic carbon emissions for large, medium-sized, and small cities and intercity traffic arteries based on the road network. The results show that in 2019, Beijing has the highest road carbon emissions reaching 19.91 million t CO2 which is almost three times that of Shijiazhuang, the capital city of Hebei province. Dwellers' commuting usually results in an increase of 2.9%, 3.3%, and 4.5% on weekdays compared with weekends for Tianjin, Shijiazhuang, and Beijing, respectively. As for the intercity road, the daily traffic flow is about 1.92 million vehicles, leading to total carbon emissions of 22.97 million t CO2. In addition, the reduction potential of carbon emissions in Beijing is evaluated. If the average road speed is increased to 0.9Vf (road design speed) during the morning rush hour in Beijing from 7 a.m. to 8 a.m., the road emission reduction could reach 57.85%. [ABSTRACT FROM AUTHOR]

Carbon emissions increase the risk of climate change. As one of the primary sources of carbon emissions, road traffic faces a significant challenge in terms of reducing carbon emissions. Many studies have been conducted to examine the impacts of cities on carbon emissions from the perspectives of urbanization, population size, and economics. However, a detailed understanding of the relationship between road traffic and urban carbon emissions is lacking due to the lack of a reasonable set of road traffic metrics. Furthermore, there have been fewer studies that have conducted cluster analyses of the impact factors, which will be supplemented in this research. We established 10 impact metrics, including the highway network system, city road network system, public transit system, and land use system of streets and transportation, using 117 county-level cities in Hebei Province as the study area, which is one of the regions in China with the most acute conflicts between economic development and the environment. We built an ordinary least squares (OLS) model, a spatial lag model (SLM), a spatial error model (SEM), a spatial Durbin model (SDM), and a geographically weighted regression (GWR) model, and performed a cluster analysis on the key metrics. The results are as follows: (1) The difference in spatial distribution of urban land-average carbon emissions is obvious, highly concentrated in the areas surrounding Beijing and Tianjin. (2) The GWR model has a higher R2 and a lower AICc than global models (OLS, SLM, SEM, and SDM) and performs better when analyzing the impact mechanism. (3) Highway network density, city road length, and density of the public transit network have significant effects on urban land-average carbon emissions, whereas the street and transportation land use systems have no significant effect, which indicates that the highway network and public transit systems should be prioritized. (4) The GWR model results show that the impact of the four metrics on the urban land-average carbon emissions exhibits clear spatial heterogeneity with a significant piecewise spatial distribution pattern. The highway network density has a relatively large impact on the northern region. The northwest is more affected by the density of the public transit network. The southwest is most impacted by the length of city roads. (5) The study area is divided into four distinct characteristic areas: the highway network dominant impact area, the public transit dominant impact area, the city road network dominant impact area, and the multi-factor joint impact area. Different traffic optimization strategies are proposed for different areas. [ABSTRACT FROM AUTHOR]

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

Investigating the linkage between financial development (FD) and carbon emissions is important for mitigating climate change. Nevertheless, there is a scarcity of studies investigating how carbon emissions decouple from FD. Here, we investigate the relationship between FD and carbon emissions by using the decoupling model based on cross-province data of China during 2000–2019. Then, we use the decomposition method to analyze the nine drivers of decoupling elasticity of FD and CO2 emissions. We found that China experienced weak decoupling and strong negative decoupling in most years. Only the finance develops at a very high level; the FD had spare capacities to promote the reduction in the carbon emissions. For example, several developed provinces (e.g., Tianjin, Zhejiang, Guangdong) realized strong decoupling after 2012. The reduction in energy intensity and the increase of foreign direct investment promoted the decoupling of FD from carbon emissions. During the financial recession period, developing a bank-based financial market helped the emissions reduction. Once financial crisis is overcome, developing a market-based financial market promoted the decoupling of FD from emissions. This is because that with the fast FD, the development of stock market contributed to emission reductions through technological improvement, while the bank loans inhibited the decoupling process through the expansion of capital-labor inputs. Overall, these results help in the assessment of the emissions impacts of FD and in addressing climate change problems. [ABSTRACT FROM AUTHOR]

Scientific prediction of net carbon emissions in the Beijing–Tianjin–Hebei (BTH) region is of significance to examine carbon emission reduction in the context of the "double carbon" target. In this study, the carbon peak and carbon neutrality states in the BTH region are determined through a regional double carbon target analysis framework, and a logistic chaotic sparrow search algorithm backpropagation neural network hybrid model (LCSSA-BP) optimized by a logistic chaotic sparrow search algorithm (LCSSA) is used to forecast the net carbon emissions. The findings reveal that the net carbon emissions in the BTH region generally increased during the study period; Beijing, Tianjin, and Hebei are in different stages toward the realization of the double carbon target; population size, affluence, and urbanization rate are positively correlated, whereas the proportion of foreign direct investment and energy intensity is negatively associated with net carbon emissions; the prediction accuracy of the LCSSA-BP is superior to that of the SSA-BP and BP neural network, and it can be used to forecast the net carbon emissions in the BTH region. [ABSTRACT FROM AUTHOR]

The emission of greenhouse gases, especially CO2, is the main factor causing global warming. Due to incomplete statistical data on energy consumption at and below the urban scale of Beijing-Tianjin-Hebei (BTH), in this study, Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) and Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) data were combined, and a neural network model and weighted average method based on DN (Digital Number) value were used to obtain CO2 emissions at the municipal and county scales with a resolution of 1 km × 1 km from 2000–2019. Next, a spatial-temporal analysis model and spatial econometric model were used to study the CO2 emissions at different scales of BTH. This study also solved the problem that STIRPAT analysis cannot be carried out due to insufficient urban statistical CO2 emissions data. The results show that the energy CO2 emissions in BTH present a distribution pattern of "East greater than West", with a trend of first rising and then slowing down. Moreover, the rapid growth areas are mainly located in Chengde and Tianjin. The degree of regional spatial aggregation decreased year by year from 2000–2019. Population, affluence and technology factors were positively correlated with CO2 emissions in Tianjin and Hebei. For Beijing, in addition to foreign investment, factors such as urbanization rate, energy intensity, construction and transportation factors all contributed to the increase in CO2 emissions. Among them, the growth of population is the main reason for the increase of CO2 at the urban scale in BTH. Finally, based on the research results and the specific situation of the cities, corresponding policies and measures are proposed for the future low-carbon development of the cities. [ABSTRACT FROM AUTHOR]

• A novel adjustable dual receiver for dish-Stirling cogeneration system is proposed. • A new accurate formula to calculate the focal spot diameter is proposed. • Control strategy of generation and heating switching based on irradiance is built. • Technical and economic indicators of the cogeneration system are evaluated. • Comprehensive energy efficiency can be increased by 55.83%. With the increasing global demand for carbon emission reduction, solar energy has received great attention worldwide, with solar dish Stirling system as a representative technology. However, some shortcomings of the conventional design limit its commercial application. In this paper, a novel solar dish Stirling cogeneration system (SDSCS) with adjustable dual receiver is proposed. A control strategy for switching between power generation and heat production modes is established, which is based on direct normal irradiance (DNI). In order to verify the system performance, three regions with significant differences in DNI levels in Tianjin, Yinchuan, and Lhasa were selected for comparison of technical and economic indicators. The results show that, compared with the conventional system that can only generate electricity, the SDSCS can achieve a significantly higher proportion of system running time in three regions at 14.66%, 24.53%, and 34.32%, respectively. The comprehensive energy efficiency shows the same trend at 55.83%, 53.91%, and 41.03%. In addition, the load satisfaction rate calculated by the day of the three regions is 43.05%, 81.46%, and 94.04%, respectively. In terms of levelized energy cost, the value of using SDSCS is 6.77 CNY/(kW·h), 3.37 CNY/(kW·h), and 1.94 CNY/(kW·h), lower than conventional system except for Tianjin. [ABSTRACT FROM AUTHOR]

Cities are usually the final destination for consumable goods and services produced along supply chains and the most appropriate scale to implement policy. Consumption-based policies could complement current carbon emissions mitigation actions, but such studies at the city level are relatively rare. We used a demand-driven input-output model to explore a historical time series (1987–2012) of consumption-based emissions in Tianjin for the first time, a typical industrial city which has the largest carbon footprint in China. The results reveal the differences between consumption- and production-based emissions, and Tianjin has transformed from a producer city into a typical consumer city since 2000s, mainly due to infrastructure construction. There is more capital investment in industrial infrastructures than in real estate in Tianjin, causing the largest carbon footprint. The trade deficit and different carbon intensity have substantial influences on consumption-based emissions. Finally, population, income, and urbanization could enable a more accurate interpretation of urban carbon footprint growth. Demand-driven policy implications for addressing these emissions in booming industrial cities are discussed and provide a new perspective on carbon emissions mitigation. Our results offer valuable lessons on industrial cities' strategies and initiatives for climate change mitigation worldwide, particularly in developing countries. [ABSTRACT FROM AUTHOR]

The assessment of the social cost of carbon (SCC) is an important basis for formulating regional differentiated climate policies. This paper attempts to assess future SCC for eight Chinese regions by using the regional integrated model of climate and the economy (the RICE model), based on four scenarios, namely baseline (BS), carbon emissions trading (CET), carbon quotas (CQ), and carbon tax (CT) for the period 2005–2050. The results show that under the BS, CET, and CT scenarios, the SCC of China's eight regions increases by $22.26/t C, $20.58/t C, and $19.14/t C, respectively, from 2005 to 2050. The CT scenario performed best in terms of impact on CEs and SCC, but in order to do so they will have to bear certain economic losses. There are differences between the SCC of the eight Chinese regions under different scenarios. Under the BS, CET, and CT scenarios, the areas where SCC is highest are distributed in the Central, North Coast, and Southwest regions, and in the CQ scenario, the high-SCC areas are concentrated in the Southwest, North Coast, and Northeast regions. The low-SCC areas are distributed in the Beijing–Tianjin and South Coast regions under all four scenarios. The central and western regions will become the major energy consuming regions after 2025, so they will face a greater pressure than other regions to reduce emissions. • The RICE model is used to calculate SCC in eight Chinese regions from 2005 to 2050. • Regional economic and carbon emission trends are simulated under four scenarios. • Central and western regions will have highest energy consumption in the future. • SCC has meaningful spatial differences across the eight Chinese regions. [ABSTRACT FROM AUTHOR]

• Based on the life cycle assessment theory, this paper has estimated and analyzed carbon emissions of 207 residential buildings in Tianjin. • Correlation analysis and elastic net were jointly used to select 12 design factors as independent variables for predictive models. • Four frequently-used regression techniques, PCR, RF, MLP and SVR, were used to develop regression models of carbon emissions. Many studies have been conducted on life cycle assessment and control measures for carbon emissions of buildings. Methods proposed by these studies usually require not only specific accounting model, but also detailed inventory data, which is not available at early design stage. Seeing that the importance of design phase to carbon emissions during building's lifecycle, a study on regression model of carbon emissions using designing factors was done. Firstly, based on process analysis method, the carbon emissions of 207 residential buildings in Tianjin were calculated. The results show that annual carbon emissions per floor area are between 30 and 60 kgCO 2 /(m2•year), with manufacture phase and operation phase accounting for 11%–25% and 75%–87%, respectively. Then, correlation analysis and elastic net were used to determine 12 designing factors for predictive model; At last, four regression techniques, PCR, RF, MLP and SVR were used to develop regression models, respectively; comparison and process analysis of model development were given later. The results show that SVR has the optimal predictive accuracy among four models, its corresponding coefficient of determination can reach to 0.800. This regression model can be utilized to estimate carbon emissions based on designing factors, which can help designers make a strategic decision at early stage. [ABSTRACT FROM AUTHOR]