Activity Data and Emission Factor for Forestry and Other Land Use Change Subsector to Enhance Carbon Market Policy and Action in Malawi

Activity data and emission factors are critical for estimating greenhouse gas emissions and devising effective climate change mitigation strategies. This study developed the activity data and emission factor in the Forestry and Other Land Use Change (FOLU) subsector in Malawi. The results indicate that “forestland to cropland,” and “wetland to cropland,” were the major land use changes from the year 2000 to the year 2022. The forestland steadily declined at a rate of 13,591 ha (0.5%) per annum. Similarly, grassland declined at the rate of 1651 ha (0.5%) per annum. On the other hand, cropland, wetland, and settlements steadily increased at the rate of 8228 ha (0.14%);5257 ha (0.17%);and 1941 ha (8.1%) per annum, respectively. Furthermore, the results indicate that the “grassland to forestland” changes were higher than the “forestland to grassland” changes, suggesting that forest regrowth was occurring. On the emission factor, the results interestingly indicate that there was a significant increase in carbon sequestration in the FOLU subsector from the year 2011 to 2022. Carbon sequestration increased annually by 13.66 ± 0.17 tCO2 e/ha/yr (4.6%), with an uncertainty of 2.44%. Therefore, it can be concluded that there is potential for a Carbon market in Malawi.

Research on Spatial-Temporal Characteristics and Driving Factor of Agricultural Carbon Emissions in China

Macroscopic grasp of agricultural carbon emissions status, spatial-temporal characteristics as well as driving factors are the basic premise in further research on China’s agricultural carbon emissions. Based on 23 kinds of major carbon emission sources including agricultural materials inputs, paddy ifeld, soil and livestock breeding, this paper ifrstly calculated agricultural carbon emissions from 1995 to 2010, as well as 31 provinces and cities in 2010 in China. We then made a decomposed analysis to the driving factors of carbon emissions with logarithmic mean Divisia index （LMDI） model. The results show：（1） The amount of agricultural carbon emissions is 291.1691 million t in 2010. Compared with 249.5239 million t in 1995, it increased by 16.69%, in which, agricultural materials inputs, paddy ifeld, soil, enteric fermentation, and manure management accounted for 33.59, 22.03, 7.46, 17.53 and 19.39%of total agricultural carbon emissions, respectively. Although the amount exist ups and downs, it shows an overall trend of cyclical rise; （2） There is an obvious difference among regions：the amount of agricultural carbon emissions from top ten zones account for 56.68%, while 9.84%from last 10 zones. The traditional agricultural provinces, especially the major crop production areas are the main source regions. Based on the differences of carbon emission rations, 31 provinces and cities are divided into ifve types, namely agricultural materials dominant type, paddy ifeld dominant type, enteric fermentation dominant type, composite factors dominant type and balanced type. The agricultural carbon emissions intensity in west of China is the highest, followed by the central region, and the east zone is the lowest; （3） Compared with 1995, efifciency, labor and structure factors cut down carbon emissions by 65.78, 27.51 and 3.19%, respectively;while economy factor increase carbon emissions by 113.16%.

Spatial-temporal Evolution Characteristics and Decoupling Analysis of Influencing Factors of China’s Aviation Carbon Emissions

The aviation industry has become one of the top ten greenhouse gas emission industries in the world. China’s aviation carbon emissions continue to increase, but the analysis of its influencing factors at the provincial level is still incomplete. This paper firstly uses Stochastic Impacts by Regression on Population, Affluence and Technology model(STIRPAT) model to analyze the time series evolution of China’s aviation carbon emissions from 2000 to 2019. Secondly, it uses the Logarithmic Mean Divisia Index(LDMI) model to analyze the influencing characteristics and degree of four factors on China’s aviation carbon emissions, which are air transportation revenue, aviation route structure, air transportation intensity and aviation energy intensity. Thirdly, it determines the various factors’ influencing direction and evolution trend of 31 provinces’ aviation carbon emissions in China(not including Hong Kong, Macao, Taiwan of China due to incomplete data). Finally, it derives the decoupling effort model and analyzes the decoupling relationship and decoupling effort degree between air carbon emissions and air transportation revenue in different provinces. The study found that from 2000 to2019, China’s total aviation carbon emissions continued to grow, while the growth rate of aviation carbon emissions showed a fluctuating downward trend. Air transportation revenue and aviation route structure promote the growth of total aviation carbon emissions, and air transportation intensity and aviation energy intensity have a restraining effect on the growth of total aviation carbon emissions. The scope of negative driving effect of air transportation revenue and air transportation intensity on total aviation carbon emissions in various provinces has increased. While the scope of positive driving influence of aviation route structure on total aviation carbon emissions of various provinces has increased, aviation energy intensity mainly has negative driving influence on total aviation carbon emissions of each province. Overall, the emission reduction trend in the areas to the west and north of the Qinling-Huaihe River Line is obvious. The decoupling mode between air carbon emissions and air transportation revenue in 31 provinces is mainly expansion negative decoupling.The air transportation intensity effect shows strong decoupling efforts in most provinces, the decoupling effort of aviation route structure effect and aviation energy intensity effect is not prominent.

A study on the factor market distortion and the carbon emission scale effect of two-way FDI

Based on the data of 30 Chinese provinces for the period from 2004 to 2015,this paper expounds the carbon emissions effect of two-way foreign direct investment (FDI) from the perspective of scale effect and factor market distortions.This study uses Kaya identity to decompose carbon emission and construct simultaneous equations model to empirically examine the factor market distortion and the carbon emission scale effect of two-way FDI.The results show that the inward foreign direct investment (IFDI) increase regional carbon emission through scale effect and also exacerbates factor market distortion in China,whereas the outward FDI trends reduce carbon emission and reduces factor market distortions in China.The study also shows that human capital,research and development (R&D),trade openness,and capital accumulation are important determinants of two-way FDI.Therefore,the study proposes that IFDI policies should focus on acquiring green technologies.In addition,the domestic enterprises should be encouraged to participate in global business.

Spatial network structure of transportation carbon emission efficiency in China and its influencing factors

Grasping the spatial correlation structure of transportation carbon emission efficiency(TCEE)and its influencing factors is significant for promoting high-quality and coordinated development of the transportation industry and the relevant region.Based on the ideal point cross-efficiency(IPCE)model,the social network analysis method was employed herein to explore the spatial correlation network structure of China’s provincial TCEE and its influencing factors.The results obtained showed the following outcomes.(1)During the study period,China’s provincial TCEE formed a complex and multithreaded network association relationship,but its network association structure was still relatively loose and presented the hierarchical gradient characteristics of dense in the east and sparse in the west.(2)The correlation of China’s TCEE formed a block segmentation based on the regional boundaries,and its factional structure was relatively obvious.The eastern region was closely connected with the central region,and generally connected with the western and northeastern regions.The central region was mainly connected with the eastern and western regions,and relatively less connected with the northeastern region.Besides,the northeastern region was weakly connected with the western region.(3)Shanghai,Beijing,Zhejiang,Guangdong,Jiangsu,Tianjin,and other developed provinces were in the core leading position in the TCEE network,which significantly impacted the spatial correlation of TCEE.However,Heilongjiang,Jilin,Xinjiang,Qinghai,and other remote provinces in the northeast and northwest were at the absolute edge of the network,which weakly impacted the spatial correlation of TCEE.(4)Provincial distance,economic development-level difference,transportation intensity difference,and transportation structure difference had significant negative impacts on the spatial correlation network of China’s provincial TCEE.In contrast,the energy-saving technology level difference had a significant positive impact on it.The regression coefficients of transportation energy structure and environmental regulation differences were positive but insignificant;their response mechanism and effects need to be improved and enhanced.

Impact of Greenization on the Marginal Utility of Intensity of Carbon Emissions and Factors Affecting it in China

The impact of greenization on the marginal utility of the intensity of carbon emissions in China and factors influ-encing this relationship are explored in this study.China’s level of greenization is evaluated by using an index system developed based on the comprehensive index method.The intensity of carbon emissions is determined by using the standards for the coefficients of conversion of coal equivalent and coefficients of carbon emission.The impact of greenization on the marginal utility of the intensity of carbon emissions is then evaluated by using an elastic formula and factors affecting this relationship are verified by regression analysis.The results are as fol-lows:(1)China’s level of greenization has exhibited a trend of constant increase.The intensity of carbon emissions has followed a continual downward trend while the impact of greenization on the marginal utility of the intensity of these emissions has been declining.(2)Urbanization as well as scientific and technological developments have slowed the reduction in the marginal utility of the intensity of carbon emissions,whereas the structure of energy consumption has expedited it under the diminishing impact of greenization.The standard of living of the popu-lation,intensity of environmental regulation,and environmental quality have had different influences under dif-ferent conditions.

Low Carbon Beijing:Research on the Influencing Factors of Carbon Emission Trading Price

The international community has taken extensive actions to achieve carbon neutrality and sustainable development with the intensification of global warming and climate change.China has also carried out a long-term layout,setting the goal of achieving a carbon peak by 2030 and carbon neutrality by 2060.In 2021,with the official launch of a unified national carbon emissions trading market,China’s nationwide carbon emissions trading kicked off.Carbon emission trading is an important policy tool for China’s carbon peak and carbon-neutral action and an essential part of the country’s promotion of a comprehensive green transformation of the economy and society.This study uses a VAR(Vector Autoregressive)model to analyze the influencing factors of the Beijing carbon emissions trading price from January 2014 to December 2019.The study found that coal prices have the most significant impact on Beijing’s carbon emissions trading prices.Oil prices,industrial development indexes,and AQI(Air Quality Index)impacted Beijing’s carbon emissions trading prices.In contrast,natural gas prices and economic indexes have the most negligible impact.These findings will help decision-makers determine a reasonable price for carbon emissions trading and contribute to the market’s healthy development.

Carbon Emission Trends of Manufacturing and Influencing Factors in Jilin Province,China

This paper constructed a carbon emission identity based on five factors: industrial activity, industrial structure, energy intensity, energy mix and carbon emission parameter, and analyzed manufacturing carbon emission trends in Jilin Province at subdivided industrial level through Log-Mean Divisia Index(LMDI) method. Results showed that manufacturing carbon emissions of Jilin Province increased 1.304 × 107 t by 66% between 2004 and 2010. However, 2012 was a remarkable year in which carbon emissions decreased compared with 2011, the first fall since 2004. Industrial activity was the most important factor for the increase of carbon emissions, while energy intensity had the greatest impact on inhibiting carbon emission growth. Despite the impact of industrial structure on carbon emissions fluctuated, its overall trend inhibited carbon emission growth. Further, influences of industrial structure became gradually stronger and surpassed energy intensity in the period 2009–2010. These results conclude that reducing energy intensity is still the main way for carbon emission reduction in Jilin Province, but industrial structure can not be ignored and it has great potential. Based on the analyses, the way of manufacturing industrial structure adjustment for Jilin Province is put forward.

Study on Carbon Emission Driving Factors and Low-carbon Policy of Transportation Industry in Henan Province

The transportation industry is the basic industry of national economic development. At the same time, it is the only industry in China that has continuously increased CO2 emissions, and the high energy consumption problems have not been solved. Henan province, as a major energy consumption province, it is urgent to improve the supply quality of transportation industry. The paper uses the IPCC calculation method for carbon emission to calculate the CO2 emissions of transportation industry in Henan province from 2004 to 2014. Then, this paper uses the LMDI method to decompose the CO2 emission in the transportation industry and give the proposal to reduce the CO2 emission according to the CO2 emission drivers.

Analysis and forecast of residential building energy consumption in Chongqing on carbon emissions

Carbon emissions mainly result from energy consumption. Carbon emissions inevitably will increase to some extent with economic expansion and rising energy consumption. We introduce a gray theory of quantitative analysis of the energy consumption of residential buildings in Chongqing,China,on the impact of carbon emission factors. Three impacts are analyzed,namely per capita residential housing area,domestic water consumption and the rate of air conditioner ownership per 100 urban households. The gray prediction model established using the Chongqing carbon emission-residential building energy consumption forecast model is sufficiently accurate to achieve a measure of feasibility and applicability.

Analysis of transportation carbon emissions and its potential for reduction in China

The transportation industry is an essential sector for carbon emissions mitigation.This paper firstly used the LMDI(Logarithmic Mean Divisia Index)decomposition method to establish factors decomposition model on China's transportation carbon emission.Then,a quantitative analysis was performed to study the factors influencing China's transportation carbon emissions from 1991 to 2008,which are identified as transportation energy efficiency,transportation structure and transportation development.The results showed that:(1)The impact of transportation development on transportation carbon emissions showed pulling function.Its contribution value to carbon emissions remained at high growth since 1991 and showed an exponential growth trend.(2)The impact of transportation structure on transportation carbon emissions showed promoting function in general,but its role in promoting carbon emissions decreased year by year.And with the continuous optimization of transportation structure,the promoting effect decreased gradually and showed the inversed"U"trend.(3)The impact of transportation energy efficiency on transportation carbon emissions showed a function of inhibition before pulling.In order to predict the potential of carbon emission reduction,three scenarios were set.Analysis of the scenarios showed that if greater intensity emission reduction measures are taken,the carbon emissions will reduce by 31.01 million tons by 2015 and by 48.81 million tons by 2020.

Greenhouse gas emission analysis and measurement for urban rail transit: A review of research progress and prospects

Rail transit plays a key role in mitigating transportation system carbon emissions.Accurate measurement of urban rail transit carbon emission can help quantify the contribution of urban rail transit towards urban transportation carbon emission reduction.Since the whole life cycle of urban rail transit carbon emission measurement involves a wide range of aspects,a systematic framework model is required for analysis.This research reviews the existing studies on carbon emission of urban rail transit.First,the characteristics of urban rail transit carbon emission were determined and the complexity of carbon emission measurement was analyzed.Then,the urban rail transit carbon emission measurement models were compared and analyzed in terms of the selection of research boundaries,the types of greenhouse gas(GHG)emissions calculation,and the accuracy of the measurement.Following that,an intelligent station was introduced to analyze the practical application of digital collaboration technology and energy-saving and carbon-reducing system platforms for rail transit.Finally,the urgent problems and future research directions at this stage were discussed.This research presents the necessity of establishing a dynamic carbon emission factor library and the important development trend of system integration of carbon emission measurement and digital system technology.

Field emission of carbon nanotube array with normal-gate cold cathode

A hexagon pitch carbon nanotube （CNT） array vertical to the normal gate of cold cathode field emission displayer （FED） is simulated by solving the Laplace equation. The calculated results show that the normal gate causes the electric field around the CNT tops to be concentrated and the emission electron beam becomes a column. The field enhancement factor and the emission current intensity step up greatly compared with those of the diode structure. Emission current density increases rapidly with the decrease of normal-gate aperture. The gate voltage exerts a critical influence on the emission current.

Research on Measurement and Calculation of Carbon Emission from the Production of Prefabricated Building Components

The measurement and calculation of the carbon emission from the production of prefabricated building components were studied.Based on the carbon emission factor method,a carbon emission calculation model of the components in the production phase was established.Besides,the actual measurement method and calculated at rated power method were proposed for the measurement and calculation of carbon emission,and several measurements were carried out in a component factory located in a coastal area of south China and a component factory located in Beijing,respectively.The results of the study show that the carbon emission factors of laminates and wallboards produced by factories located in coastal areas of southern China under natural curing conditions were 7.61 kg CO2/m3 and 5.84 kg CO2/m3 respectively.The carbon emissions conversion coefficients of concrete mixer,reinforcing bar production line and travelling crane between actual operation and with per the rated power were approximately 0.44,0.34 and 0.34 respectively.When the actual measurement cannot be performed,the conversion coefficient can be used to correct the data of the calculated at rated power to make it closer to the true value.The carbon emission factor of the laminated panels produced by the component factory in Beijing under steam curing concrete conditions was 132.15 kg CO2/m3,and the factory is used as a prototype,a complementary steam generation system model of solar energy and boiler was established,and it was calculated that the system can reduce CO2 emissions by about 300 tons throughout the year.

Industrial carbon emissions and influencing factors in the Yangtze River Delta urban agglomeration

This paper calculates the industrial carbon emissions of the Yangtze River Delta urban agglomeration over the period 2006-2013. An empirical analysis is conducted to find out the influencing factors of industrial carbon emissions of the Yangtze River Delta urban agglomeration, using a spatial Durbin panel model. The results show that cities with larger industrial carbon emissions often enjoy low annual growth rates, while the cities with smaller ones enjoy higher annual growth rate; There exists a comparatively strong positive correlation in space in per capita carbon emission; urbanization, and total population. GDP per capita and international trade are the main influencing factors of industrial carbon emissions; There are spatial spillover effects on international trade and urbanization of neighboring cities, which have a significant impact on local industrial carbon emissions.

Relationship Between Built Environment, Socio-economic Factors and Carbon Emissions from Shopping Trip in Shenyang City, China

Promoting active travel behavior and decreasing transport-related carbon dioxide(CO_2) emissions have become a priority in many Chinese cities experiencing rapid urban sprawl and greater automobile dependence. However, there are few studies that holistically examine the physical and social factors associated with travel CO_2 emissions. Using a survey of 1525 shoppers conducted in Shenyang, China, this study estimated shopping-related travel CO_2 emissions and examined how the built environment and individual socioeconomic characteristics contribute to shopping travel behavior and associated CO_2 emissions. We found that, firstly, private car trips generate nearly eight times more carbon emissions than shopping trips using public transport, on average. Second, there was significant spatial autocorrelation with CO_2 emissions per trip, and the highest carbon emissions were clustered in the inner suburbs and between the first and second circumferential roads. Third, shopping travel CO_2 emissions per trip were negatively correlated with several built environment features including population density, the quantity of public transport stations, road density, and shop density. They were also found to be significantly related to the individual socio-economic characteristics of car ownership, employment status, and education level using a multinomial logistic regression model. These empirical findings have important policy implications, assisting in the development of measures that contribute to the sustainability of urban transportation and meet carbon mitigation targets.

Multi-process production occurs in the iron and steel industry,supporting‘dual carbon'target:An in-depth study of CO_(2)emissions from different processes

Reducing CO_(2)emissions of the iron and steel industry,a typical heavy CO_(2)-emitting sector is the only way that must be passed to achieve the‘dual-carbon’goal,especially in China.In previous studies,however,it is still unknown what is the difference between blast furnace basic oxygen furnace(BF-BOF),scrap-electric furnace(scrap-EF)and hydrogen metallurgy process.The quantitative research on the key factors affecting CO_(2)emissions is insufficient There is also a lack of research on the prediction of CO_(2)emissions by adjusting industria structure.Based on material flow analysis,this study establishes carbon flow diagrams o three processes,and then analyze the key factors affecting CO_(2)emissions.CO_(2)emissions of the iron and steel industry in the future is predicted by adjusting industrial structure The results show that:(1)The CO_(2)emissions of BF-BOF,scrap-EF and hydrogen metallurgy process in a site are 1417.26,542.93 and 1166.52 kg,respectively.(2)By increasing pellet ratio in blast furnace,scrap ratio in electric furnace,etc.,can effectively reduce CO_(2)emissions(3)Reducing the crude steel output is the most effective CO_(2)reduction measure.There is still 5.15×10^(8)-6.17×10^(8) tons of CO_(2)that needs to be reduced by additional measures.

The accounting method and application of CO_2 emissions responsibility by the electricity sector at the provincial level in China

When accounting the CO_2 emissions responsibility of the electricity sector at the provincial level in China,it is of great significance to consider the scope of both producers' and the consumers' responsibility,since this will promote fairness in defining emission responsibility and enhance cooperation in emission reduction among provinces.This paper proposes a new method for calculating carbon emissions from the power sector at the provincial level based on the shared responsibility principle and taking into account interregional power exchange.This method can not only be used to account the emission responsibility shared by both the electricity production side and the consumption side,but it is also applicable for calculating the corresponding emission responsibility undertaken by those provinces with net electricity outflow and inflow.This method has been used to account for the carbon emissions responsibilities of the power sector at the provincial level in China since 2011.The empirical results indicate that compared with the production-based accounting method,the carbon emissions of major power-generation provinces in China calculated by the shared responsibility accounting method are reduced by at least 10%,but those of other power-consumption provinces are increased by 20% or more.Secondly,based on the principle of shared responsibility accounting,Inner Mongolia has the highest carbon emissions from the power sector while Hainan has the lowest.Thirdly,four provinces,including Inner Mongolia,Shanxi,Hubei and Anhui,have the highest carbon emissions from net electricity outflow- 14 million t in 2011,accounting for 74.42% of total carbon emissions from net electricity outflow in China.Six provinces,including Hebei,Beijing,Guangdong,Liaoning,Shandong,and Jiangsu,have the highest carbon emissions from net electricity inflow- 11 million t in 2011,accounting for 71.44% of total carbon emissions from net electricity inflow in China.Lastly,this paper has estimated the emission factors of electricity consumption at the provincial level,which can avoid repeated calculations when accounting the emission responsibility of power consumption terminals(e.g.construction,automobile manufacturing and other industries).In addition,these emission factors can also be used to account the emission responsibilities of provincial power grids.

An Approach to Carbon Emissions Prediction Using Generalized Regression Neural Network Improved by Genetic Algorithm

The study on scientific analysis and prediction of China’s future carbon emissions is conducive to balancing the relationship between economic development and carbon emissions in the new era,and actively responding to climate change policy.Through the analysis of the application of the generalized regression neural network(GRNN)in prediction,this paper improved the prediction method of GRNN.Genetic algorithm(GA)was adopted to search the optimal smooth factor as the only factor of GRNN,which was then used for prediction in GRNN.During the prediction of carbon dioxide emissions using the improved method,the increments of data were taken into account.The target values were obtained after the calculation of the predicted results.Finally,compared with the results of GRNN,the improved method realized higher prediction accuracy.It thus offers a new way of predicting total carbon dioxide emissions,and the prediction results can provide macroscopic guidance and decision-making reference for China’s environmental protection and trading of carbon emissions.

Potential and Efficiency of Agricultural Pollution Control in China and Its Influential Factors

Agricultural pollution has become the dominant source of water pollution in China and the carbon reduction in agricultural aspect is pressing.Based on list analysis method,the COD,TN and TP in agriculture in 28 provinces in China from 1995 to 2010 were evaluated and compared.By dint of directional distance function,the economics mechanism to reduce carbon emission was discussed.The reduction efficiency and potential of three kinds of pollutants were estimated.The regression indicates that the educational degree,income level and work play a crucial role in carbon emission.