• Energy Research

Eco-tourism has become increasingly popular in the postmodern era. However, the management of tourism waste remains a major challenge for tourist destinations worldwide. Here, a non-participatory survey was conducted in five famous scenic spots in Yarlung Zangbo Grand Canyon National Park in Motuo County, Tibet, to characterize the waste composition and the amount of average daily production per capita during sightseeing. In addition, interviews were conducted at 26 restaurants and 32 hotels in Motuo Town (the administrative center of Motuo County), and data on the composition and amount of average daily production per capita of waste generated by tourists during accommodation and meals were obtained. The total amount of tourism waste in Motuo County in 2018 was approximately 172,108.82 kg. Based on the data collected, an emergy analysis was applied to emergy calculations of the pollution and losses generated during two conventional and locally used tourism waste disposal methods. According to China's emergy to money ratio (EMR) of 2018, the emergy was converted into its monetary value. The theoretical ecological compensation standard for Motuo County was 4,293,568.99 CNY (equivalent to 648,830.20 USD), and the average ticket price for a single tourist was 18.87 CNY (equivalent to 2.85 USD) in the absence of government fiscal transfer payments. These findings should be utilized by local national park authorities to establish a market-oriented ecological compensation mechanism that is capable of alleviating environmental pressure.

As a major carbon emitter in China, the emission mitigation in industrial sector performs great significance for China to achieve its emission reduction targets. Using the provincial panel data during 2000-2016 of China's industrial sector, this paper first used a gravity model to study the spatial distribution and center of gravity of industrial CO2 emissions. Then, an integrated decomposition approach based on Shephard distance functions was adopted to study the driving factors of industrial carbon intensity. Results indicate that during 2000-2016, industrial CO2 emissions center of gravity gradually moved to the west. China's industrial carbon intensity achieved considerable decline, with the annual change rate of 8.27%. The energy intensity decline, technology progresses of both production and energy saving were the most important factors facilitating carbon intensity decline. However, energy structure adjustment exerted positive effects in carbon intensity increase, although its effects were minor. Industrial carbon intensity witnessed decrease in almost all provinces except Xinjiang. The effects resulted from various factors were also different across provinces. Finally, suggestions were proposed to further decrease industrial carbon intensity.

Abstract Cities are the main carbon dioxide (CO 2 ) emitters in China, and it is important to explore both the characteristics and reduction potential of their CO 2 emissions. This paper calculates the industrial energy-related CO 2 emissions (IECEs) of 17 cities in China's Yangtze River Delta (YRD) region from 2005 to 2014 and analyses the driving factors of CO 2 emissions using the Logarithmic Mean Divisia Index (LMDI). In addition, this paper predicts the CO 2 reduction potential of this group of cities for the period from 2015 to 2020. The results show that (1) during the sample period, industrial CO 2 emissions in the studied cities increased by a factor of 1.61. Economic output is the greatest contributor, followed by population size. Energy intensity and energy structure are the two main emission mitigation factors; (2) the driving factors of CO 2 emissions in the group of cities exhibit distinct spatial characteristics, indicating that future analyses of cities in this area should have distinctly different foci; and (3) the forecasting results show that under moderate and aggressive scenarios, CO 2 emissions in the studied cities can be reduced by 281.59 Mt and 711.90 Mt, respectively, by 2020.

Abstract China's mining and quarrying industry is characterized by “high pollution, high energy consumption, and high emissions.” Improving this sector's green total factor productivity (TFP) is of great importance for furthering the sustainable development of China's economy. Using a global data envelopment analysis (DEA), this paper analyzes the green TFP of China's mining and quarrying industry for the period of 1991–2014 with regard to technology, scale, and management. The following results are found. First, during the sample period, the green TFP of China's mining and quarrying industry increased by 71.7%. Technological progress was the most important contributor, and the decline in scale efficiency and management efficiency were two inhibitors. Fortunately, in recent years, management efficiency has gradually improved and become a new impetus for green TFP growth. Second, the characteristics of the green TFPs in the sub-industries vary considerably. During the sample period, the green TFPs of the mining and processing of ferrous metal ores (MPFMO), the mining and processing of non-ferrous metal ores (MPNFMO), and the mining and processing of nonmetal ores (MPNO) grew rapidly and became the benchmarks, whereas those of the mining and washing of coal (MWC) and the extraction of petroleum and natural gas (EPNG) remained very low. Third, the returns to scale of the sub-industries also varied. EPNG, MPNFMO, MPNO were in the stage of increasing returns to scale or constant returns to scale during the entire period, whereas MWC and MPFMO have recently entered the stage of decreasing returns to scale.

As the driving force of modern economic development, the evolution of the global energy patterns during major emergencies deserves attention. This study aims to uncover changes in global energy patterns during the COVID-19 epicenter storm. The results reveal concentrated energy trade volume in a few flows, with high-income countries holding greater influence. Amid the epicenter storm, there was a contraction in global energy trade volume, and trading patterns underwent significant shifts: (1) the global natural gas trading center shifted from the Middle East to western Africa initially, then to the United States and Russia; (2) Brazil, a crucial crude oil exporter, saw a weakened position in global crude oil exports; and (3) in the coal trading network, Australia and Russia maintained their position as trading centers throughout the storm. Swift policy adaptation is crucial for resilience and stability amid evolving global energy patterns.

Abstract This paper used an extended logarithmic mean Divisia index (LMDI) approach to decompose the changes of China's industrial CO2 emissions into four traditional factors and three investment and R&D expenditure related factors, including: carbon dioxide emissions coefficient effect, energy structure effect, energy intensity effect, R&D efficiency effect, R&D intensity effect, investment intensity effect, and industrial activity effect. The results show that: (1) industrial activity was the largest stimulating factor in industrial CO2 emissions. Investment intensity and R&D intensity changes displayed overall positive effects in emissions growth but with some fluctuations in different periods and provinces. (2) Energy intensity was the prominent factor to facilitate emissions-reduction, followed by the R&D efficiency. The two factors contributed to a considerable decrease in industrial CO2 emissions. (3) Among all factors, energy structure effect was the weakest, and showed alternative influencing directions in different periods and provinces. (4) The effects exerted from various factors were distinctly varied in different economic stages and provinces. And generally, the curbing effects cannot counteract the promoting effects. Finally, the empirical results show that continue to decrease energy intensity, facilitate the investment and R&D efforts aiming at energy-saving and emission-reduction, and reduce the reliance on coal while further raise the renewable energy utilization are beneficial to alleviate industrial CO2 emissions.

Abstract Green development has attracted increasing attention by the international community. This paper uses a green development performance index (GDPI) based on data envelopment analysis (DEA) and the panel data of 41 regions (including 165 countries/sub-regions) to estimate the global patterns of green development performance and its influencing factors. The results show that: (1) the patterns of the global green development are extremely imbalanced. Developed regions/countries have been leading in green development since the 21th century, while most of the developing regions/countries’ GDPIs are relatively low and are following a descending path; (2) an U-shaped Environmental Kuznets Curve (EKC) exists between GDPI and economic development level and the inflection point is 2424 US $; (3) GDPI is positively related to living altitude, energy structure, and integrated oil prices while negatively related to ecological carrying capacity; (4) the financial crisis occurred since the second half of 2007 has a negative influence on the global green development.

Abstract The primary objective of this paper is to explore the main factors driving CO 2 emissions in China's mining industry and its subsectors. By utilizing the Log-Mean Divisia Index (LMDI) decomposition method, this paper decomposes changes in energy-related CO 2 emissions into an industrial scale effect, an energy intensity effect, an energy structure effect, and a CO 2 emissions coefficient effect in both the overall mining industry and its subsectors. The main results indicate that (1) CO 2 emissions in China's mining industry rose by approximately 95.74 million tons during 2000–2014. The mining and washing of coal (MWC) and the extraction of petroleum and natural gas (EPNG) were the top two emitters, accounting for approximately 80% of total CO 2 emissions in China's mining industry. (2) From the overall mining industry perspective, the industrial scale effect made the largest contribution to increasing CO 2 emissions, while the energy intensity effect was the most important factor in reducing CO 2 emissions. Additionally, the energy structure effect played a small role in inhibiting CO 2 emissions. (3) From the subsector perspective, the industrial scale effect increased CO 2 emissions in five subsectors, especially the MWC, while the energy intensity effect was the dominant factor inhibiting CO 2 emissions in the five subsectors. The energy structure effect impacted CO 2 emissions negatively in the EPNG, the MWC and the mining and processing of non-ferrous metal ores (MPNFMO); it impacted CO 2 emissions positively in the mining and processing of ferrous metal ores (MPFMO) and the mining and processing of non-metal ores (MPNO). Policy recommendations related to energy intensity and energy structure adjustments were provided.