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Addressing the environmental risks related to contamination of groundwater with the phenolics, benzene, toluene, ethyl benzene, xylene (BTEX) and polycyclic aromatic hydrocarbons (PAHs), which might be potentially released from the underground coal gasification (UCG) under adverse hydrogeological and/or operational conditions, is crucial in terms of wider implementation of the process. The aim of this study was to determine the main organic pollutants present in the process condensate generated during the UCG trial performed on hard coal seam in the Experimental Mine ‘Barbara’, Poland; 8,933 L of condensate was produced in 813 h of experiment duration (including 456 h of the post-process stage) with average phenolics, BTEX and PAH concentrations of 576,000, 42.3 and 1,400.5 μg/L, respectively. The Hierarchical Clustering Analysis was used to explore the differences and similarities between the samples. The sample collected during the first 48 h of the process duration was characterized by the lowest phenanthrene, anthracene, fluoranthene and pyrene contents, high xylene content and the highest concentrations of phenolics, benzene, toluene and ethyl benzene. The samples collected during the stable operation of the UCG process were characterized by higher concentrations of naphthalene, acenaphthene, fluorene, phenanthrene, anthracene, fluoranthene, pyrene, benzo(a)anthracene, chrysene, while in the samples acquired in the post-process stage the lowest concentrations of benzene, toluene, naphthalene, acenaphthene and fluorene were observed.

The Chinese electric power industry, including its coal industry and other energy industries that are not efficient, contributes to China’s serious energy shortages and environmental contamination. The governing authority considers energy conservation to be one of the most prominent national targets, and has formulated various plans for decarbonizing the power system. Applying the trans-log cost function, this paper examined the trans-log cost function to analyze the potential inter-factor substitution among energy, capital and labor. We also investigated what role human capital played in energy substitution for the electric power sector during the period from 1981 to 2017. Three key results were derived: (1) energy is price-insensitive, (2) there exists large substitution sustainability between both capital and labor with energy, and (3) human capital input not only enhances the extent of energy substitutability with capital and labor but also is a substitute to energy itself. These findings imply that the liberalization of the electric price mechanism is conducive to lessening energy use and augmenting non-energy intensiveness, and that energy conservation technology could become more sustainable by investing more capital in the electricity sector, thereby achieving a capital–energy substitution and a decrease of CO2 emissions. We further suggest that the priority for the Chinese electric power industry should be to attach more importance to increasing human capital input.

SignificanceEstimates of nutrient allocation in different plant tissues and the relationships between the nutrient contents and photosynthetic capacity are critical to predicting ecosystem carbon sequestration under global change. Here, we provide an assessment of large-scale patterns of community-level nitrogen and phosphorus concentrations in different plant tissues and then examine how nutrient allocations are coupled with plant productivity. The results show that nutrient concentrations in leaves are less responsive to abiotic environments than those in woody stems and roots (stable leaf nutrient concentration hypothesis); the relationships between vegetation primary productivity and leaf nutrient contents are stronger when less nutrients are allocated to the woody tissues (productivity–nutrient allocation hypothesis) and are stronger in deciduous than in evergreen vegetation (productivity–leaf lifespan hypothesis).

AbstractThe concept of a ‘just transition’ to a low‐carbon economy is firmly embedded in mainstream global discourses about mitigating climate change. Drawing on Karl Polanyi's political economy elaborated inThe Great Transformation, we interrogate the idea of a just transition and place it within its historical context. We address a major contradiction at the core of global energy transition debates: the rapid shift to low‐carbon energy‐systems will require increased extraction of minerals and metals. In doing so, we argue that extractive industries are energy and carbon‐intensive, and will enlarge and intensify social and ecological injustice. Our findings reveal the importance of understanding how the idea of a just transition is used, and by who, and the type of justice that underpins this concept. We demonstrate the need to ground just transition policies and programmes in a notion of justice as fairness.

Societal relations in rural areas have entered into a new stage of adjustment over the past decade. However, the adjustment, which might bring about profound societal changes in countryside as well as in China as a whole, have not been paid much attention and very few studies have been conducted from the perspective of ecological resource crises. We use the case of a village as an example to show how water pollution, as one of the contributory factors, possibly affect the transition of clans and societal changes in Chinese villages. Through observation and interviews, we find that there is an apparent rise of “New Clanism” within clans, which gradually abandons the tradition of supremacy of clan interests and places family or individual interests at top priority. We also find that clan boundaries get increasingly obscure since the integrity of clans is undermined by the rise of new interest groups across clans, but the boundaries remain relatively clear due to the consistency (albeit incomplete) of clan interests. Some new clan élites and representatives of new interest groups get involved in village governance, which indicates that their goals have shifted from natural resources to social or political capital. The significance of our findings is that they provide not only a unique perspective for the interaction between society and resources, but also some new ideas for the future study of rural China at the environment-social interface.

We researched China's climate and sustainable development goal with relevant and susceptible instruments capable of inducing and mitigating carbon emissions. Amidst the contributor to the global carbon emissions, China is caught in between mitigating its carbon emission and aiming towards placing its national contribution of emissions to the acceptable levels of 1.5 °C and below 2 °C. Following the intricacies surrounding China's sustainable development as it contains its economic and environmental performance, we adopt China's data of 1980 and 2018 with different scientific approaches (nonlinear autoregressive distributed lag (NARDL), dynamic ordinary least square test, and bootstrap Granger causality) with different instruments (such as economic growth, financial development, renewable energy, and innovation policies) to research China's sustainable development. For clear exposition and insight into our findings with policies attached, we draw a conclusion from the outcomes of the mentioned approaches. From NARDL and dynamic ordinary least squares (DOLS), we find that economic growth through economic activities is statistically significant in determining the trend (increase) of carbon emissions in China in both periods (short run and long run). However, other selected instruments (financial, renewable, and innovation policies) tend towards controlling and moderating the carbon emissions in China. Thus, China has good prospects to mitigate its carbon emissions if considered tailoring its policies towards favorable instruments. From bootstrap Granger causality, we find similar inferential results that support previous findings thereby confirming the positive implication of the selected instruments to China's sustainable development. Hence, the nexus that is established among the selected instruments clearly show the importance of technological innovation and renewable energy in mitigating carbon emissions.

The global energy demand is increasing due to climate changes and carbon usages. Accumulating evidences showed energy sources using offshore wind from the sea can be added to increase our consumption capacity in long term. In addition, building offshore wind farms can also be environmentally advantageous compared to onshore farms. The assessment of wind energy resources is crucial for the site selection of wind farms. Currently, short-term wind forecast models have been developed to predict the wind power generation. However, methods are needed to improve the forecasting accuracy for ever-changing weather data. So, we try to use deep learning methods to predict long-term wind energy for identifying potential offshore wind farms. The experimental results indicate that PredRNN++ prediction model designed from the spatiotemporal perspective is feasible to evaluate long-term wind energy resources and has better performance than traditional LSTM.

In recent years, the energy consumption structure has been accelerating towards clean and low-carbon globally, and China has also set positive goals for new energy development, vigorously promoting the development and utilization of renewable energy, accelerating the implementation of renewable energy substitution actions, and focusing on improving the consumption capacity of new energy. However, due to the intermittent and unstable characteristics of renewable energy, it is difficult to meet the demands of the power load side in practical applications. Energy storage is an important link for the grid to efficiently accept new energy, which can significantly improve the consumption of new energy electricity such as wind and photovoltaics by the power grid, ensuring the safe and reliable operation of the grid system, but energy storage is a high-cost resource. Therefore, this paper focuses on the energy storage scenarios for a big data industrial park and studies the energy storage capacity allocation plan and business model of big data industrial park. Firstly, based on the characteristics of the big data industrial park, three energy storage application scenarios were designed, which are grid center, user center, and market center. On this basis, an optimal energy storage configuration model that maximizes total profits was established, and financial evaluation methods were used to analyze the corresponding business models. Finally, taking an actual big data industrial park as an example, the economic viability of energy storage configuration schemes under two scenarios was discussed, and an energy storage system construction plan was proposed to promote the zero-carbon target of the big data industrial park.