Energy Consumption of Rail Baltica Project: Regional Aspects of Environmental Impact

Methane emissions along biomethane and biogas supply chains are underestimated

Net-zero emissions chemical industry in a world of limited resources

The COVID-19 epidemic and the Russian–Ukrainian conflict have led to a global food and energy crisis, making the world aware of the importance of agroforestry development for a country. Modern agriculture mechanization leads to massive energy consumption and increased CO2 emissions. At the same time, China is facing serious demographic problems and a lack of consumption in the domestic market. The Chinese government is faced with the dilemma of balancing environmental protection with economic development in the context of the “double carbon” strategy. This article uses annual World Bank statistics from 1990 to 2020 to study the asymmetric relationships between agroforestry development, energy consumption, population size, and economic development on CO2 emissions in China using the partial least squares path model (PLS-PM), the autoregressive VAR vector time series model, and the Granger causality test. The results are as follows: (1) The relationship between economic development and carbon dioxide emissions, agroforestry development and carbon dioxide emissions, energy consumption and carbon dioxide emissions, and population size and carbon dioxide emissions are both direct and indirect, with an overall significant positive effect. There is a direct negative relationship between population size and carbon dioxide emissions. (2) The results of the Granger causality test show that economic development, energy consumption, and CO2 emissions are the causes of the development of agroforestry; economic development, agroforestry development, population size, and CO2 emissions are the causes of energy consumption; energy consumption is the cause of economic development and CO2 emissions; and agroforestry development is the cause of population size and energy consumption. (3) In the next three years, China’s agroforestry development will be influenced by the impulse response of economic development, energy consumption, and CO2 emission factors, showing a decreasing development trend. China’s energy consumption will be influenced by the impulse response of economic development, agroforestry development, population size, and CO2 emission factors, showing a decreasing development trend, followed by an increasing development trend. China’s CO2 emission will be influenced by the impulse response of energy consumption and agroforestry development. China’s CO2 emissions will be influenced by the impulse response of energy consumption and agroforestry development factors, showing a downward and then an upward development trend.

Carbon dioxide emissions intensity of Portuguese industry and energy sectors: A convergence analysis and econometric approach

Correct emission factors are necessary for evaluating vehicle emissions and making proper decisions to manage air pollution in the transportation sector. In this study, using a chassis dynamometer at the Automotive Emission Laboratory, CO2 and CH4 emission factors of light-duty vehicles (LDVs) were developed by fuel types and driving speeds. The Bangkok driving cycle was used for the vehicle’s running and controlling under the standard procedure. Results present that the highest average CO2 and CH4 emission factors were emitted from LDG vehicles, at 232.25 g/km and 9.50 mg/km, respectively. The average CO2 emission factor of the LDD vehicles was higher than that of the LDG vehicles, at 182.53 g/km and 171.01 g/km, respectively. Nevertheless, the average CH4 emission factors of the LDD vehicles were lower than those of the LDG vehicles, at 2.21 mg/km and 3.02 mg/km, respectively. The result reveals that the lower driving speed emitted higher CO2 emission factors for LDVs. It reflects the higher fuel consumption rate (L/100 km) and the lower fuel economy rate (km/L). Moreover, the portion of CO2 emissions emitted from LDVs was 99.96% of total GHG emissions. The CO2 and CH4 emission factors developed through this study will be used to support the greenhouse gas reduction policies, especially concerning the CO2 and CH4 emitted from vehicles. Furthermore, it can be used as a database that encourages Thailand’s green transportation management system.

ASEAN is a region with high carbon dioxide (CO2) emissions, accompanied by an increase in population, gross domestic product (GDP) and energy consumption. Population, GDP, and energy consumption can be linked to CO2 emissions through an identity equation called the Rich Identity. This research is based on Kaya identity to describe CO2 emissions to calculate the impact of population, economic activity, energy intensity and carbon intensity on CO2 emissions in ASEAN and 8 ASEAN countries (i.e., Indonesia, Malaysia, Singapore, Thailand, Philippines, Vietnam, Myanmar and Brunei Darussalam) from 1990 to 2017. The method used is the Logarithmic Mean Division Index (LMDI). The data used are from the International Energy Agency (IEA) and the World Bank. Four effects measured and main findings showed that population, economic activity and carbon intensity factor increased by 293.02 MtCO2, 790.0 MtCO2, and 195.51 MtCO2, respectively. Meanwhile, energy intensity effect made ASEAN's CO2 emissions decrease by 283.13 MtCO2. Regarding contributions to the increase in CO2 emissions in all ASEAN countries, the population effect increases CO2 emissions in all countries in ASEAN and the economic activity effect is also the same, except in Brunei Darussalam which makes CO2 emissions in this country decreased by 1.07 MtCO2. Meanwhile, the effects of energy and carbon intensity are different. The effect of energy intensity causes CO2 emissions in lower-middle income countries to decrease, while in upper-middle and high-income countries, it increases carbon emissions. In contrast to the effect of carbon intensity, that actually makes CO2 emissions increase in lower-middle income countries and reduces carbon emissions in upper-middle and high-income countries.

Low-carbon production of iron and steel: Technology options, economic assessment, and policy

Quantifying the trends and affecting factors of CO2 emissions under different urban development patterns: An econometric study on the Yangtze river economic belt in China

Population growth and trends are centrally important to the environment because it helps to determine the environmental impact of human activities. In this study, the World Bank database has been used. Here, carbon dioxide (CO2) emissions, and energy intensity (EI) are considered as environmental indicators. The population indicators are the proportion of the population aged 15-64 years, and the percentage of the urban population. The Gross Domestic Product (GDP) is considered a development indicator in a country. This study tries to identify the association between population environment and development. Correlation analysis has been employed to know association and Path analysis is used to determine the important factors for environmental impacts such as carbon dioxide (CO2) emissions. The result presents that the zero-order correlation exists among energy intensity (EI), the proportion of the population aged 15-64 (P15-64), urbanization (UR), gross domestic product (GDP) per capita (US$), total population (P) ) and carbon dioxide (CO2) emission in Bangladesh and India. It is observed that 8 paths for Bangladesh and 7 paths for India out of each 12 hypothesized paths are found to be statistically significant. In Bangladesh, the total effects of exogenous variables like as energy intensity (X1) and population aged 15-64 (X2) are observed negative direction on carbon dioxide emissions (X6) and the remaining variable like as urbanization (X3) is observed as positive direction on carbon dioxide emissions. However, in India total effects of these two exogenous variables population aged 15-64 (X2) and urbanization (X3) are observed positive direction on carbon dioxide emissions (X6) and the remaining variable like as energy intensity (X1) is observed negative direction on carbon dioxide emissions (X6). The total effects of endogenous variables like as GDP per capita (X4) show a negative direction on carbon dioxide emissions and population (X5) shows a positive direction on carbon dioxide emissions. The study demonstrates that CO2 emission is important for environmental impact in Bangladesh and India. There is a strong association between population, GDP per capita, energy consumption and urbanization and CO2 emission in Bangladesh and India. The factors of CO2 emissions play an important role in environmental degradation. Thus, attention should be focused on using low energy consumption, and proper urbanization, particularly on modern technology which assures fewer uses of CO2 emissions in Bangladesh and India.

دراینمقاله،میزانو ارزش انتشارگازهایگلخانه‌ای اکسید‌نیتروس(N2O) و دی‌اکسید‌کربن(CO2)حاصلازتولید حبوبات منتخب ایران (شامل نخود، لوبیا و عدس) با استفاده از مدل GHGE،برایسالزراعی91-90برآورد شده است.نتایج نشان‌داد که استان‌هایفارسوبوشهر، به‌ترتیبباتولیدسالانه271/79 و 004/0 تنN2O، بیشترینوکمترینمیزانتولیدگاز گلخانه‌ایN2Oرا دارامی‌باشند. همچنین استان‌هایلرستانوبوشهر نیز به‌ترتیب باتولیدسالانه83/10327 و33/1‌تنCO2،بیشترینوکمترینمیزانتولیدگاز گلخانه‌ایCO2را به‌خود اختصاص داده‌اند. مجموعهزینه‌هایزیست‌محیطی انتشار گازهای گلخانه‌ای N2O و CO2 کلکشورنیزحدود705/32‌میلیاردریالبرآوردگردید. باتوجهبه یافته‌ها، مدیریت کودهای نیتروژنه مصرفی در مزارعوتوسعهسیاست‌کاهشمیزانانتشاربه‌همراه مالیات زیست‌محیطی انتشار گازهای گلخانه‌ای بر سطوح مختلف تولید پیشنهاد شده ‌است. واژه‌های کلیدی: اکسید‌نیتروس، دی‌اکسید‌کربن، حبوبات، گازهای گلخانه‌ای

Carbon Dioxide Emissions Intensity (CDEI) in Iraq correlated between carbon dioxide emission (kg CO2) and crude oil production (COP) (kg oil equivalent). This relationship is important for industry and energy sectors to the achievement of their economic and environmental goal, then to know a common pattern of emissions intensity. The sources of data set from Carbon Dioxide Information analysis center (CDIAC), contain: total CO2 emission, COP from Iraqi Ministry of oil and Iraqi crude oil production increased over time and about (more than 80%) from Basra city. Environmental Kuznets Curve (EKC) was calculated.CDEI was nonlinear behavior that high level in the 1970s then decreased to reach 1.707 kg co2 / kgoil equivalent in 1997, and CDEI was more sensitive to COP than total CO2 emissions. EKC maximum values present in early 1970s and in 2004 present highest value was (0.082 metric ton / current US$ person). COP was unstable level, fluctuation between (1-3) mb/d, till reach 4.29 mb/d as average in 2019.

Agricultural practices contribute significant levels of greenhouse gas (GHG) emissions. Methods to measure net global warming potential (GWP) and greenhouse gas intensity (GHGI) that account for all sources and sinks of GHG emissions in agroecosystems are still evolving. Sources of GHGs include soil CO 2 , N 2 O, and CH 4 emissions and CO 2 emissions associated with farm operations, N fertilization, and other chemical inputs. Sinks of GHGs include CH 4 uptake, soil C sequestration, and crop residue returned to the soil. This chapter discusses the methods of measuring net GWP and GHGI using two approaches: In the soil organic C (SOC) method, net GWP and GHGI are calculated by using N 2 O and CH 4 emissions (or CH 4 uptake), as well as CO 2 emissions from farm operations, N fertilization, and other chemical inputs as GHG sources and C sequestration rate (ΔSOC) as GHG sink. In the soil respiration method, soil respiration (excluding root respiration) is included as another GHG source, and the previous year's crop residue returned to the soil instead of ΔSOC is included as GHG sink in addition to the above parameters. Advantages and drawbacks of each method of calculating net GWP and GHGI are also discussed.

An empirical research on the influencing factors of regional CO2 emissions: Evidence from Beijing city, China

The 2008–2009 financial crisis, often referred to as the Great Recession, presented one of the greatest challenges to economies since the Great Depression of the 1930s. Before the financial crisis, and in response to the Kyoto Protocol, many countries were making great strides in increasing energy efficiency, reducing carbon dioxide (CO2) emission intensity and reducing their emissions of CO2. During the financial crisis, CO2 emissions declined in response to a decrease in economic activity. The focus of this research is to study how energy related CO2 emissions and their driving factors after the financial crisis compare to the period before the financial crisis. The logarithmic mean Divisia index (LMDI) method is used to decompose changes in country level CO2 emissions into contributing factors representing carbon intensity, energy intensity, economic activity, and population. The analysis is conducted for a group of 19 major countries (G19) which form the core of the G20. For the G19, as a group, the increase in CO2 emissions post-financial crisis was less than the increase in CO2 emissions pre-financial crisis. China is the only BRICS (Brazil, Russia, India, China, South Africa) country to record changes in CO2 emissions, carbon intensity and energy intensity in the post-financial crisis period that were lower than their respective values in the pre-financial crisis period. Compared to the pre-financial crisis period, Germany, France, and Italy also recorded lower CO2 emissions, carbon intensity and energy intensity in the post-financial crisis period. Germany and Great Britain are the only two countries to record negative changes in CO2 emissions over both periods. Continued improvements in reducing CO2 emissions, carbon intensity and energy intensity are hard to come by, as only four out of nineteen countries were able to achieve this. Most countries are experiencing weak decoupling between CO2 emissions and GDP. Germany and France are the two countries that stand out as leaders among the G19.

Comparison of carbon dioxide emissions intensity of steel production in China, Germany, Mexico, and the United States