도로 종류와 도로생애주기별 탄소배출량, 에너지소모량 및 비용에 대한 거시적 분석방법

Household carbon emissions are important components of total carbon emissions. The consumer side of energy-saving emissions reduction is an essential factor in reducing carbon emissions. In this paper, the carbon emissions coefficient method and Consumer Lifestyle Approach (CLA) were used to calculate the total carbon emissions of households in 30 provinces of China from 2006 to 2015, and based on the extended Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, the factors influencing the total carbon emissions of households were analyzed. The results indicated that, first, over the past ten years, the energy and products carbon emissions from China’s households have demonstrated a rapid growth trend and that regional distributions present obvious differences. Second, China’s energy carbon emissions due to household consumption primarily derived from the residents’ consumption of electricity and coal; China’s products household carbon emissions primarily derived from residents’ consumption of the high carbon emission categories: residences, food, transportation and communications. Third, in terms of influencing factors, the number of households in China plays a significant role in the total carbon emissions of China’s households. The ratio of children 0–14 years old and gender ratio (female = 100) are two factors that reflect the demographic structure, have significant effects on the total carbon emissions of China’s households, and are all positive. Gross Domestic Product (GDP) per capita plays a role in boosting the total carbon emissions of China’s households. The effect of the carbon emission intensity on total household carbon emissions is positive. The industrial structure (the proportion of secondary industries’ added value to the regional GDP) has curbed the growth of total carbon emissions from China’s household consumption. The results of this study provide data to support the assessment of the total carbon emissions of China’s households and provide a reasonable reference that the government can use to formulate energy-saving and emission-reduction measures.

This study examines the nexus between business strategy and carbon emissions by utilising a dataset of U.S. firms from 2007 to 2020. It focuses on two broad types of firms, that is, prospectors and defenders. Regarding carbon emissions, we consider total emissions (Scope 1 & 2), direct emissions (Scope 1) and indirect emissions (Scope 2). The results reveal a significant association between business strategy and total carbon emissions as well as direct carbon emissions. Notably, the results suggest that prospectors, compared to defenders, display higher levels of total and direct carbon emissions. Our findings contribute to the debate on whether prospectors in developed countries mismanage sustainability issues. The study offers valuable insights into the interplay between business strategy and carbon emissions and provides empirical evidence that business strategy is an important determinant of total and direct carbon emissions.

Effectively exploring the impacts of urban spatial structures on carbon dioxide emissions is important for achieving low-carbon goals. However, most previous studies have examined the impact of urban spatial structure on total carbon emissions based only on polycentricity. Fine-grained studies on subsectoral carbon emissions and other dimensions of urban spatial structure are lacking. Therefore, our study comprehensively explores the impact of urban dispersion and polycentricity on total carbon emissions and carbon emissions of four subsectors (industry, power, civilian, and transportation) from 2012 to 2017 while considering the effects of city size. Results reveal that the nighttime light data is useful for measuring urban spatial structure, and a polycentric, decentralized urban spatial structure correlates with the reduced total carbon emissions and transportation carbon emissions. Meanwhile, a decentralized urban spatial structure gives rise to lower industrial carbon emissions and civilian carbon emissions, whereas a multicenter urban spatial structure contributes to minimizing carbon emissions from power systems. However, in small and medium-sized cities, urban spatial structure differently affects the total carbon and transportation carbon emissions.

Evaluating energy, health and carbon co-benefits from improved domestic space heating: A randomised community trial

PurposeThe growth of the Indian economy is accompanied by the rising trend of energy utilisation and its devastating effect on the environment. It is vital to understand the nexus between energy utilisation, climate and environment degradation and growth to devise a constructive policy framework for achieving the goal of sustainable growth. This study aims to analyse the long- and short-run association and direction of association between energy utilisation, carbon emission and growth of the Indian economy in the presence of structural break.Design/methodology/approachThe study probes the association and direction of association between variables at both aggregate (total energy utilisation, total carbon emission and gross domestic product [GDP]) and disaggregates level (coal utilisation and coal emission, oil utilisation and oil emission, natural gas utilisation and natural gas emission along with GDP) over the time period of 50 years, i.e. 1971–2020. Autoregressive distributed lag model is used to examine the association between the variables and presence of structural break is confirmed with the help of Zivot–Andrews unit root test. To check the direction of association, vector error correction model Granger causality is performed.FindingsAggregate carbon emissions are affected positively by aggregate energy consumption and GDP in both short and long run. Bidirectional causality exists between total emissions and GDP, whereas a unidirectional causality runs from energy consumption towards carbon emission and GDP in the long run. At disaggregate level, consumption of coal energy impacts positively, whereas GDP influences coal emission negatively in the long run only. Furthermore, consumption of oil and GDP influences oil emissions positively in the long run. Lastly, natural gas is the energy source that has the fewest emissions in both short and long run.Originality/valueThere is a rapidly growing body of research on the connections and cause-and-effect relationships between energy use, economic growth and carbon emissions, but it has not conclusively proved how important the presence of structural breaks or changes within the economy is in shaping the outcomes of the aforementioned variables, especially when focusing on the Indian economy. By including the impact of structural break on the association between energy use, carbon emission and growth, where energy use and carbon emission are evaluated at both aggregate and disaggregate level, the current study aims to fill this gap in Indian literature.

Energy use and greenhouse gas emissions of Larch roundwood production forestry were analysed using life cycle assessment (LCA). Application of LCA for forestry operations and forest products is a developing field of study in Korea. Larch roundwood production system comprises seedling production, silvicultural operations, felling and logging (log extraction). From the results of life cycle inventory analysis, among various forestry operations, roundwood extraction (hauling and loading of felled roundwood) had about three quarters of the total energy use and GHG emissions. The life-cycle energy use and GHG emissions of roundwood production at forest were 6.921kgCO2e/m3 and 94.02MJ/m3, respectively. The total life-cycle energy use and GHG emissions including roundwood transportation from the forest to the sawmill(roundtrip distance of 150km) were 12.232kgCO2e/m3 and 168.76MJ/m3, respectively. To reduce energy use and GHG emissions in forestry, optimization of wood production and transportation system decreasing fossil fuel use is required.

Agricultural activities emit about 10-12% greenhouse gases each year. Moreover, the total amount is on the rise, which can significantly contribute to global warming. To get governments to pay more attention to greenhouse gas emissions from agricultural activities, so that the necessary regulations can be implemented. This paper studies the relationship between agricultural land expansion and greenhouse gas emissions. My initial hypothesis was that ceteris paribus, the agricultural land expansion would cause a rise in greenhouse gas emissions. I obtained 25 years of data for 12 countries from the World Development Indicators Database of the World Bank. And created a panel data frame. The variables exist in it are: Total greenhouse gas emissions (kt of CO2 equivalent), Energy use (kg of oil equivalent per capita), Alternative and nuclear energy (% of total energy use), Agricultural land (% of land area), GDP per capita (current US$). In order to cope with the possible heteroscedasticity problem in the regression, several of the variables were processed logarithms. This paper used a Two-way Fixed effect model which controls fixed effects from both cross section and time. The explained variable is log (GHG emissions), and the explanatory variables are log (energy use per capita), log (GDP per capita), clean energy using rate, and percentage of agricultural land in total land. However due to the presence of multicollinearity, log (GDP per capita) was removed. This paper also tested the autocorrelation and used the Cochrane-Orcutt Iterative Process to estimate the autocorrelation coefficient to transform all variables so that solve the autocorrelation issue. The final regression model is as follows: ln(Greenhouse Gases emission)= + 1.06*ln(Energy use per capita) – 0.009*(Alternative and nuclear energy % of total energy use) + 0.0074*(Agricultural land % of land area). = . The overall F-test of this model is significant, and the individual t-tests of the coefficients of each variable are all significant. R2=92.18, adjR2=91.04. This shows that the explanatory power of the model is strong. This outcome is consistent with my initial hypothesis that agricultural land expansion does lead to an increase in greenhouse gas emissions. The other two coefficients are also consistent with our common sense. This result can help governments to decide on the planning of land use. If the current amount of energy used and the types of energy used do not change, a country develops agriculture, it will inevitably lead to more greenhouse gas emissions. More Importantly, it is not like industrial manufacture, normally there are few regulations and policies on agricultural GHG emissions. But the quantitative results of the model tell us that as agriculture expands, it is necessary for the government to implement regulations and policies on it.

Housing sector is one of the important greenhouse gases (GHGs) emissions which result global warming. In order to trace the energy use before and after primary energy factor of electricity adjustment, this paper analyzed the primary energy factor of electricity of the end user in Taiwan from 1982 to 2009 first. Then the compositions of the primary energy use and emissions in Taiwan housing sector were analyzed. The share of electricity use in total energy use of the housing sector was 64.2% at 2009 before primary energy factor of electricity adjustment. By using primary energy factor of the electricity demand of the year as adjustment, the share of the primary energy use of the electricity increased as 84.4%. Results of the research also showed that the annual log growth rate of the primary energy use induced by electricity use was 6.45% in Taiwan housing sector from 1982 to 2009. The total primary energy use of the housing sector in 2009 was about 5.7 times of which in 1982. Respect to emission factor of the housing sector, the research showed that the annual log growth rate of GHG Emissions of the housing sector was 7.81% from 1982 to 2009. The total GHG Emissions of the housing sector in 2009 was about 8.25 times of which in 1982. The shares of emissions from electricity in housing sector were 59.6% in 1982, and 86.6% in 2009. These results showed that the use of electricity was one of the major sources of the housing emissions growth this period.

IntroductionPopulation expansion and economic development increased global greenhouse gas emissions, leading to serious environmental degradation. China, the world's largest developing country and promoter of the “Belt and Road Initiative” (BRI), accounts for 28.8% of the world"s total energy carbon emissions. How to reduce energy consumption to achieve the “double carbon” target (i.e., carbon peaking and carbon neutrality) and promote the implementation of Green BRI is still a serious challenge that China needs to face. MethodsWe evaluated China's carbon emissions using three indicators (i.e., total carbon emission, carbon intensity, and carbon emissions effect), and used spatial analysis to reveal the spatial and temporal trends of China's carbon emissions. In addition, the LMDI model was adopted to explore the driving mechanism of carbon emissions, so as to seek a path that can achieve harmonious economic and environmental development, as well as the “double carbon” target.ResultsChina's total carbon emission increased at a rate of 226.12% from 2000 to 2019, while the carbon intensity decreased at a rate of 48.84%. Carbon emission showed a trend of increasing and then decreasing from southwest to northeast. From 2000 to 2019, the total carbon emission, Gross Domestic Product (GDP), population size and total energy consumption are growing in synergy. Economic and population effects are positively related to carbon emissions, while technology effects are negatively related to it, indicating technological innovations contribute to the reduction of carbon emissions.DiscussionSome suggestions were proposed to control carbon emissions with a view to helping policy makers to formulate relevant policies. The findings provide a scientific basis and reference for the country to achieve the “double carbon” target and the low-carbon sustainable development of BRI.

Carbon inequality in residential buildings: Evidence from 321 Chinese cities

This article presents an assessment of the impacts that policy-induced increases in cost of energy or carbon may have on energy use and emission profiles of the U.S. iron and steel industry. Time series data and engineering information are combined within a dynamic computer model to endogenously specify changes in technologies, fuel mix, and production processes. Results indicate that energy taxes shift production to electric arc furnaces and reduce total energy use more than policies that raise costs of carbon. However, both energy taxes and costs of carbon will result in a similar decrease in carbon emissions when compared to the absence of those policies.

The fuel-cycle energy use and greenhouse gas (GHG) emissions associated with the application of fuel cells to combined heat and power (CHP) generation and combined heat, hydrogen, and power (CHHP) generation are evaluated and compared with the combustion technologies of internal combustion engines and microturbines, as well as with the various technologies associated with hydrogen production and grid-electricity generation in the United States. Two types of fuel cells are considered in this analysis: a phosphoric acid fuel cell (PAFC) capable of following either heat or electric load and a molten carbonate fuel cell (MCFC) that typically follows the electric load. Three types of facilities (hospital, large office building, and warehouse) are examined in two different climatic regions (Chicago and Los Angeles) to span a wide range of electric-to-heat load ratios. Two different approaches for fuel cycle analysis of CHP and CHHP applications are considered in this analysis: a total demand approach and a displacement approach. The total demand approach provides an accurate assessment of the impact of actual demand on total energy use and GHG emissions, while the displacement approach projects the potential for more energy savings and GHG emissions benefits relative to the supply of electricity from the grid generation mix. The fuel cycle results are primarily impacted by the efficiencies of hydrogen production and electric power generation, as well as by the utility factor of the co-produced heat. The energy use and GHG emissions associated with the electric power generation represent the majority of the fuel-cycle’s total energy use and emissions for all pathways. More energy and GHG emissions benefits are realized from fuel cell technologies with increased use of available coproduced heat. In general, CHHP systems exhibit more energy and GHG emission benefits than CHP systems for any of the investigated fuel cell technologies.

Determinants of greenhouse gas emissions from Swedish private consumption: Time-series and cross-sectional analyses

Life cycle energy (LCEA) and carbon dioxide emissions (LCCO2A) assessment of two residential buildings in Gaziantep, Turkey

Many areas in the world with chronic and intermittent water shortages rely on informal water systems for much of their daily water needs with water from tanker trucks, purchased bottled water, rainwater cisterns, or pumped well water. These alternative sources all require varying amounts of energy. Water–energy nexus studies have not yet considered environmental impacts of informal water sources, specifically from an energy intensity and carbon emissions perspective. This study compares energy use and carbon emissions per cubic meter and per capita for both formal and informal water sources for a neighborhood in Beirut Lebanon. Energy use and carbon emissions are calculated for three delivery stages per source including pumping, treatment and distribution. The results show that informal sources have the highest energy use and carbon emissions. From the total water delivered to households, they account for 83% of energy use and 72% of carbon emissions per capita, even though they only provide 23% of total delivered volume per capita. Bottled water and distribution of water by tanker trucks have the highest energy intensity values per cubic meter of all water sources. Moreover, internal building water pumping, which is not typically accounted for, takes up to 14% of total energy use and 23% of total carbon emissions per capita compared to other water sources. To address model uncertainty, we conduct a sensitivity analysis, showing that the base model presented reasonably stable results and identifying the most sensitive parameters for further research. While informal sources help communities overcome water shortages they result with negative impacts. Strategies are proposed to improve the environmental performance of the Lebanese electrical grid, reduce water losses, replace inefficient truck engines and incentivize household to invest in low carbon technologies.