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The reliance on petroleum derivatives and the increasing trend of fuel consumption in the transport sector have brought attention to biofuels as a measure to reduce greenhouse gas (GHG) emissions, enhance energy security and boost economic development. The incentive for utilizing biofuels is their potential to reduce carbon dioxide (CO2) emissions compared to fossil fuels. Biofuels are considered to be carbon-neutral, regarding the direct CO2 emissions associated with their combustion, on the grounds that the carbon dioxide absorbed by the plants through photosynthesis is equivalent to the CO2 released during their combustion. However, a part of the carbon of biofuels may have a fossil origin and therefore, it is responsible for "non-neutral" CO2 emissions. This was recognized by the Intergovernmental Panel on Climate Change (IPCC) in its guidelines for greenhouse gas inventories. Nevertheless, specific guidance of how to estimate fossil CO2 emissions per each type of biofuel, in order to facilitate the work of emission inventory compilers and practitioners, is neither available in the IPCC guidelines nor in the scientific literature. The purpose of this paper is to define a higher tier method to estimate the associated CO2 emissions with the fossil carbon content of biofuels, aiming to cover all possible biofuel types that are mainly used nowadays to replace diesel and gasoline. [ABSTRACT FROM AUTHOR]

With the rapid development of regional economy, energy consumption and carbon dioxide emission increase sharply. This study examines the relationship between carbon dioxide emissions and regional economic growth. Based on the mean value decomposition method of double logarithm, the model was built to identify the key indexes affecting the decomposition, and the carbon emission formula was calculated to obtain the regional carbon dioxide emission. Based on the actual data, the kuznets curve model is established to represent the relationship between CO2 emission and regional economic growth in logarithm form. The unit root test, co-integration test and granger causality test are carried out for the correlation sequences in the kuznets curve model in order to realize the research on the relationship between CO2 emission and regional economic growth. This paper discusses the relationship between carbon dioxide emission and regional economic growth through an example. It shows that the relationship between carbon dioxide emission and regional economic growth is inversely u-shaped, and there is a kuznets curve between the two. According to the result of empirical analysis, the paper puts forward some suggestions to reduce energy consumption and improve energy efficiency, which is helpful to improve social and economic structure and promote social and economic development. [ABSTRACT FROM AUTHOR]

Based on provincial panel data from 2000 to 2014, this paper measured the total factor energy efficiency by adopting the dynamic slacks-based measure model, and then employed the spatial econometric model to analyze the impact of regional mitigation of carbon dioxide on energy efficiency from both intra-regional and inter-regional perspectives in China. The results show the following: first, the average of total factor energy efficiency presents a rising trend from 2000 to 2014, and it shows a significant difference among regions. Second, the direct effect of carbon productivity on energy efficiency is positive, but the indirect effect is negative. The share ratio of direct and indirect effects is 1: −0.96; the total effect is positive but insignificant. Based on this conclusion, the paper provides some relevant policy suggestions and measures for regionally saving energy resources and relieving environmental constraints. [ABSTRACT FROM AUTHOR]

To achieve China's CO2 emissions targets, all Chinese provinces need to ensure that their CO2 emissions are maintained at a reasonable level to avoid the shortboard effect. This paper proposed an integrated method, the LSTM-STIRPAT, to predict the CO2 emissions in 30 provinces, and assess the drivers of a different region. We divide 30 provinces according to the prediction result into provinces with peak value(PWP) and provinces without peak value(PWTP) and found that (i) Inner Mongolia, Jiangxi, Shandong, Hainan, Chongqing, Guizhou, Qinghai, Xinjiang are failed to reach their CO2 emissions peak by 2030, but almost all provinces experienced a small peak in their carbon emissions from 2008 to 2013; (ii) The ranking of CO2 emissions influencing factors in the PWTP is energy intensity (+) > population density (+) > energy consumption (+) > urbanization rate (−) > GDP per capita (+) > ratio of secondary industry (+); the ranking of CO2 emissions influencing factors in the PWP is energy intensity (+) > ratio of secondary industry (+) > urbanization rate (−) > population density (+) > energy consumption (+) > GDP per capita (−); (iii) PWTP's CO2 emissions show a significant lag effect, of which the ratio of secondary industry accounts for the most significant impact. According to the research results, we put forward relevant targeted measures to achieve China's carbon emissions peak commitments in 2030: (1) PWTP should give priority to encouraging the development of technology and strengthening the utilization of new energy and renewable energy; (2) PWP should give priority to reducing energy intensity, optimizing the industrial structure and accelerating the process of urbanization; (3) CO2 emission reduction in PWTP is a long-term task, it is necessary to adhere to the optimization and adjustment of the industrial structure. [ABSTRACT FROM AUTHOR]

The Cuajimalpa campus of the Autonomous Metropolitan University (UAM) is located in the western region of Mexico City. In 2016, its global average population (students, faculty and staff) was around 2750 people. Campus policies include sustainability as one of its main aims. To evaluate and eventually reduce the environmental impact of the campus, its greenhouse gas (GHG) inventory was assessed and the carbon footprint was calculated, using the GHG protocol (GHGP): Scope 1: direct GHG emissions; Scope 2: indirect GHG emissions; and Scope 3: other indirect GHG emissions, on a calendar year basis. Scope 1 includes mobile and stationary sources and leakage of refrigerants; Scope 2 includes electrical energy usage; Scope 3 includes consumption of paper, food, water, gases, cleaning products, solvents, wastewater treatment, municipal and hazardous wastes and academic travel. In 2016, the campus produced around 3000 tons of CO2 equivalent, with Scope 1, 2 and 3 accounting for 4%, 24% and 72%, respectively. Emissions analysis by activity indicated 51% for commuting; 24% for electricity usage; 14% for academic travel; 11% for other activities. The inventory will aid the establishment of policies for reduction and mitigation of GHG, resulting in environmental and potential economic benefits. [ABSTRACT FROM AUTHOR]

A reduction in the CO2 emission intensity (ton per gross domestic product) from a base year to a target year with different rates of reduction in each individual year can yield different cumulative CO2 emissions over time, which could have different effects on the climate. In this paper, an indicator is proposed to measure the extent to which the cumulative CO2 emissions are generated by a specific reduction trajectory of CO2 emission intensity, especially for emerging economies with a relatively higher economic growth rate. The proposed indicator is important for less-developed countries and regions in which economic growth is heavily reliant on energy and in which significant potential exists for improvement of energy efficiency. The indicator is applied to evaluate the CO2 emission reduction performance in China's 12th Five-Year Plan period, and the result is not encouraging, especially when the likely gap between the energy conservation goals and temperature goals is further examined. Mitigation goals should be set by considering not only the performance in the final year, but also the reduction in cumulative emissions over the evaluation period. [ABSTRACT FROM AUTHOR]

Nowadays, as China has become the factory of the world, China's industrial sectors have become the country's main source of energy consumption and emissions including CO2, SO2, NOx and soot. This article will use the non-radial directional dispersion function model (DDF) to explore ways to improve both the energy efficiency and the emission efficiency of China's industrial sectors. Based on current research, this article proposes a new projected input for the treatment of industrial waste gas, and optimizes the projected input and output settings. The data analysis shows that there is room for improvement in China's energy efficiency, and its regional imbalances have expanded. The Chinese government is urged to pay more attention to the improvement of energy efficiency in the central and western regions, and to provide more policy support. Simultaneously, the paper attemtps to establish measurement indexes to improve the efficiency of emission reduction. As a result, the authors suggest that increasing government expenditure can play a positive role in the overall improvement of emission reduction efficiency. [ABSTRACT FROM AUTHOR]

The present study investigates the long-run impact of financial development, energy consumption and economic growth on greenhouse gas emissions for India, in the presence of endogenous structural breaks, over the period 1971–2013. The autoregressive distributed lag bounds testing procedure (ARDL) and Hatemi-J threshold cointegration technique were used to test the variables for cointegration. ARDL bounds test did not confirm any cointegrating relationship between the variables. The threshold cointegration test establishes the presence of long-run impact of financial development, energy use and economic growth on greenhouse gas emissions in India. The results reveal that the long-run relationship between the variables has witnessed two regime shifts, in 1978 and 2002. The empirical evidence shows that financial sector development and energy consumption in India degrade the environment. Unlike previous studies, this paper finds no statistical evidence of a long-run relationship between economic growth and environmental deterioration. The study also challenges the existence of an environmental Kuznets curve in India. [ABSTRACT FROM AUTHOR]