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China has made remarkable achievements in reducing carbon emissions in recent years. However, there is still much reduction room before achieving carbon neutrality. In Beijing, the capital of China, it is a strategic choice to respond to global climate change by promoting green and low-carbon development. This paper calculates the carbon dioxide emissions of key industries in Beijing and analyzes the temporal evolution trend of carbon emissions. Carbon dioxide emissions in Beijing before 2030 are predicted based on the grey prediction GM (1,1) and BP neural network model. The effects of factors of carbon dioxide emissions are discussed using the threshold regression model under different economic conditions. The results show that energy consumption intensity, GDP per capita, and the ownership of civil cars have a positive impact on carbon dioxide emissions, while the number of permanent residents and urban green space areas have a negative impact on carbon dioxide emissions. These findings of carbon emission prediction and influencing factors contribute to carbon reduction path design. Related policy implications on carbon emission reduction are put forward from the aspects of promoting industrial upgrading, accelerating the construction of advanced economic structures, optimizing transportation structures, and strengthening green building development. [ABSTRACT FROM AUTHOR]

It is of great scientific value to study the spatial differences and influencing factors of carbon emission intensity (CEI) in urban agglomerations (UAs), and it also has reference significance for China in formulating energy-saving and emission-reduction policies to achieve the target of carbon neutrality. Taking 165 prefecture-level cities in 19 UAs in China from 2007 to 2019 as the research object, this study investigated the spatial differences of CEI in UAs using exploratory spatial data analysis and explored the influencing factors of CEI via Geodetector. The results showed the following: (1) The CEI of the UAs showed a downward trend. (2) The CEI of the UAs has typical spatial agglomeration characteristics, where the North comprises mainly high-high and low-high types, whereas the South is primarily high-low and low-low types. (3) The influencing factors of CEI have undergone a transformation from industrial structure to population urbanization. [ABSTRACT FROM AUTHOR]

China has clearly put forward the strategic goals of reaching the "Carbon Emission Peak" by 2030, and achieving "Carbon Neutrality" by 2060. To achieve these goals, it is necessary to precisely understand the spatial distribution characteristics of historical carbon emissions in different regions. This paper has selected a representative national-level urban agglomeration in China, the Harbin–Changchun urban agglomeration, to study the temporal and spatial distribution characteristics of carbon emissions in its counties. This paper has constructed global and local Moran's I indexes for the 103 counties in this urban agglomeration by using the carbon emission values reflected by night light data from 1997 to 2017 to perform global and local autocorrelation analysis on a spatial level. The results show that: (1) the main characteristic of carbon emission clustering in the Harbin–Changchun urban agglomeration is similar clustering; (2) the changes in carbon emissions of the Harbin–Changchun urban agglomeration have a strong correlation with relevant policies. For example, due to the impact of the "Twelfth Five-Year Plan" policies, in 2013, the global county-level Moran's I index of the carbon emissions in the Harbin–Changchun urban agglomeration decreased by 0.0598; (3) the areas where high carbon emission values cluster together ("High–High Cluster") and low carbon emission values cluster together ("Low–Low Cluster") in the Harbin–Changchun urban agglomeration are highly concentrated, and the clusters are closely related to the development level of different regions. [ABSTRACT FROM AUTHOR]

Regional energy development and approaches are significant for China's overall economic and social development. The GDP in Zhejiang province surpassed 6 trillion yuan in 2019, and its energy consumption reached 200 million tons of standard coal (tce), both of which are at the forefront of China. In order to explore the main factors of the increase in energy consumption in Zhejiang and provide essential references for energy saving and other provinces, this paper analyzes the total energy consumption and industrial sectors on the basis of the logarithmic mean divisia index (LMDI) model. Study results show that the economy's scale is the most crucial factor affecting Zhejiang's energy consumption, with a significant growth effect. In 2015, the scale effect increased energy consumption to the highest value of 14 million tce and then reduced it to 13 million tce in 2019. The impact of the population on energy consumption increased by 10 million tce from 2010 to 2019. Energy intensity reduces energy consumption by between 0.05 and 0.15 billion tce per year, which is the main factor in reducing energy consumption. The energy structure generally plays a weak positive role due to the different energy types. The decomposition of the energy consumption per unit of value added in the industrial sector showed that the intensity and structural effect primarily reduce energy consumption, for example, the metal smelting and rolling, textile printing and paper, electric power, heating, and other industries. According to the results, enterprises should enhance the intelligence and efficiency of dispatch management and emergency responses. Zhejiang should also accelerate an international oil and gas trading center and resource allocation base to reach its carbon-neutrality goal. [ABSTRACT FROM AUTHOR]

This study is the first to propose the deployment of direct air capture (DAC) systems at large airports to provide solutions for achieving carbon neutrality in aviation transportation. Here, an estimating model for carbon dioxide (CO2) emissions in the landing and take-off (LTO) phase of large airports was developed, and the suitability of deploying DAC systems at airports was evaluated by the analytic hierarchy process (AHP). This study found that the annual CO2 emissions of 52 large airports in the LTO phase are about 23 Mt, accounting for about 23% of the total CO2 emissions of civil aviation in China. The four dimensions of airport transportation conditions, meteorological conditions, space resources, and security levels had a decreasing impact on the deployment of DAC systems in that order. The airports with suitable DAC systems are mainly located in the Yangtze River Delta, the Pearl River Delta, and the Chengdu-Chongqing Airport Cluster. This study provides a theoretical basis for the deployment of DAC systems at airports, which provides new CO2 emission reduction solutions for the aviation transportation industry. [ABSTRACT FROM AUTHOR]

Air pollution and climate change are closely interlinked, once both share common emission sources, which mainly arise from fuel combustion and industrial processes. Climate mitigation actions bring co-benefits on air quality and human health. However, specific solutions can provide negative trade-offs for one side. The Portuguese Carbon Neutrality Roadmap was developed to assess conceivable cost-effective pathways to achieve zero net carbon emissions by 2050. Assessing its impacts, on air pollutant emissions, is the main focus of the present work. The bottom-up linear optimization energy system the integrated MARKAL-EFOM system (TIMES) model was selected as a modeling tool for the decarbonization scenarios assessment. The estimation of air pollutant emissions was performed exogenously to the TIMES model. Results show that reaching net zero greenhouse gas (GHG) emissions is possible, and technologically feasible, in Portugal, by 2050. The crucial and most cost-effective vector for decarbonizing the national economy is the end-use energy consumption electrification, renewable based, across all end-use sectors. Decarbonization efforts were found to have strong co-benefits for reducing air pollutant emissions in Portugal. Transport and power generation are the sectors with the greatest potential to reduce GHG emissions, providing likewise the most significant reductions of air pollutant emissions. Despite the overall positive effects, there are antagonistic effects, such as the use of biomass, mainly in industry and residential sectors, which translates into increases in particulate matter emissions. This is relevant for medium term projections, since results show that, by 2030, PM2.5 emissions are unlikely to meet the emission reduction commitments set at the European level, if no additional control measures are considered. [ABSTRACT FROM AUTHOR]