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

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

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

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

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

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

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

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

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

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]

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