Search

With the increasing urbanization but growing resource scarcities, the securing provision of fundamental resources as food, energy and water (FEW) has become a unique challenge for urban sustainability. This is not only because of continuous demand of resource imports from different regions for urban areas, but also due to the complex interrelationships among FEW systems. In such context, exploring the interactions between FEW resources and economic activities when investigating FEW provisions to meet urban demand through trade is very essential to find effective policy intervention points and priority areas for actions. This paper investigates external binding FEW resource flows with internal certain interlinkages driven by final demand of Beijing city at different nodes along their supply chains, by combing structural path analysis and multi-regional input-output model of China 2010. The results show that the key source regions present overall neighborhood pattern that Hebei, Inner Mongolia, Anhui, Jiangsu, and Shandong near Beijing are the five leading contributors of tran-regional FEW provisions. The top 20 nexus paths are identified and the most important nexus pathways start with the other services in Beijing. Besides this, the critical supply chains appear divergent directions for FEW flows, driven by food, construction and agriculture industries respectively. Moreover, the key nodes mainly concentrate on less developed regions and energy-related sectors. For example, non-metal products manufacturing in Hebei, petroleum refining and coking in Heilongjiang, and coal mining and washing in Inner Mongolia have larger impacts on all of FEW flows across the supply chains. These results are very informative to targeting our efforts to address the urban FEW nexus issue both from the perspective of supply side and demand side.

The importance of aquatic ecosystem services (ES) has been widely recognized. However, complexities among different aquatic ecosystems, the uncertainties of ES delivery mechanism and the lack of a unified accounting method from a production perspective still bring challenges for assessing aquatic ecosystem services valuation (ESV). To address these three concerns, this study develops a coherent accounting method on aquatic ESV. This includes: (1) an aquatic classification system (“source”, “process” and “sink” type); (2) an aquatic ES classification system, which considers their formation mechanisms; (3) a set of detailed ESV accounting techniques. Aquatic ES are divided into direct services, indirect services and existing services. In addition, according to the characteristics and uniqueness of different aquatic ecosystems, the importance degree of each aquatic ES is identified. Further, the aquatic ESV accounting techniques are established to reach three study goals: (1) integration with the characteristics of specific aquatic ecosystems when measuring ESV, (2) aquatic ESV assessment from supply side, and (3) unified metric. The newly developed aquatic ESV accounting is applied to aquatic ecosystems as a case study to test this method. The results show that: (1) Sichuan has the largest aquatic ESV (1.12E+23 sej/yr); (2) Tibet has the largest aquatic ESV per unit area (5.55E+11 sej/m2/yr); (3) Most China's aquatic ecosystems have microclimate regulation as their largest ESV per unit area. This study can fill several research gaps on aquatic ESV evaluation, providing also scientific suggestions on differentiated conservation and management policies applied to specific aquatic ecosystems.

Although endowed with abundant tidal resources, China's tidal power generation industry has been largely lagging behind other renewable energy alternatives such as small hydropower, photovoltaic power, wind power and biomass-based power. To probe the reasons behind this slow pace of development, an emergy evaluation and an economic analysis were conducted on the Jiangxia Tidal Power Station (JTPS), which has an installed capacity of 4.1 MW. The JTPS is the largest tidal power station in China and the fourth largest in the world. The evaluation results exhibited a total power conversion system emergy use of 1.59E+19 sej to generate 5.90E+13 J of electricity in 2014. Tidal energy only accounts for 16.54% of the total energy budget. The rest of the resources were invested to capture and convert tidal energy to electricity, thereby generating a relatively low efficiency or high transformity of 2.69E+05 sej/J. The results revealed a JTPS emergy loading ratio (ELR) and energy sustainability index (ESI) of 3.72 and 0.41, respectively, which indicates poorer environmental performance compared to other renewable energy power plants, with the exception of photovoltaic power. In addition, the economic analysis revealed a JTPS power generation cost of 2.41 CNY/kWh, which is even higher than that of concentrating solar power (CSP). The JTPS is less competitive because tidal energy has a lower quality and is more decentralized when generating electricity compared to other renewable resources. This inefficiency was exacerbated in the JTPS case due to the original multipurpose design and consequential poor site selection with respect to tidal electricity generation. These results further highlight the importance of dam site selection and the tidal range, as they are critical for the improvement of the tidal power generation environmental performance. More importantly, R&D is essential to promote the development of this technology and to ultimately utilize abundant tidal resources in an efficient and sustainable way.

The regional energy management body has a large amount of regional industrial companies’ energy consumption data. It can evaluate the energy utilization of listed regional industrial companies based on the total data and, then, find the key points for understanding the resources usage patterns, identifying the problematic companies, and establishing good energy consumption practices. This paper reviews the research progress on big data analysis and industrial energy efficiency evaluation and focuses on the energy efficiency evaluation methods based on energy consumption process analysis and big data mining approach. Based on K-means and multi-dimensional association rules algorithm, to analyze the characteristics of regional energy consumption in different industries and companies, we cluster single industry in K-means and finding their levels of water and energy consumption. This classification provided us a reference point to identify the industries and companies to focus on and locate the bad consumption practices and environmental performance. Then, multi-dimensional association rules are used to find the correlation of processes, companies and energy efficiency to guide the energy conservation in regional energy monitor. The output of our research is a working Big Data analytics platform and the results generated from advance analytics techniques applied specifically to solve regional energy efficiency problems.

China's vast untapped offshore wind energy and the concentration of electricity consumers in coastal regions make offshore wind power a promising solution to the country's cleaner power transition. However, the potential energy and environmental emissions of offshore wind farms have not been thoroughly investigated. Thus, this study employed a process-based life cycle inventory (LCI) model to calculate the life-cycle energy and emissions of offshore wind power in China based on the country's first offshore wind energy project. Results showed that the life-cycle energy of the studied wind farm was 2.28E+09 MJ, or 0.39 MJ/kwh, with emission intensities of 25.5 g CO2-eq/kWh for GHG, 0.02 g/kWh for PM2.5, 0.06 g/kWh for SO2 and 0.09 g/kWh for NOx. The life-cycle footprints are dominated by the manufacture of wind turbines and foundation materials production. Compared to onshore wind farms, offshore wind power plants usually have greater life-cycle electricity yields, but their levelized energy and environmental footprints are less favorable. The green manufacturing of China's steel sector, and scientific operation and maintenance programs of wind facilities contribute to greening offshore wind power. Results of this study facilitate robust policy making of government authorities and contribute to the green deployment of offshore wind technologies in China.

The study of carbon metabolism from the perspective of a network has received much attention in ecological simulation. The present study focused on the previous analysis of natural metabolism, and we generated a spatial network model with 18 nodes consisting of natural and technical metabolic components. We combined flow and utility analysis using the ecological network method to quantitatively analyze the structure of the ecological relationships and summarize the key metabolic functions among different relationships of Beijing. The positive or negative effect of these relationships on the system was also explored. Furthermore, we used the GIS method to map the different relationships and examine their flux to characterize the spatial variations of Beijing. We concluded that transportation and industrial land, cultivated and urban land were the main components that contributed to exploitation and control relationships, resulting in excessive carbon emissions during urban sprawl. Natural metabolic components and cultivated land were the foundation of mutualism relationships that increase carbon sequestration. Beijing needs more space to develop mutualism relationships while cutting down on the carbon emission brought by the competition relationships. The results will guide the optimization of the spatial structure in urban areas, and the well-constructed framework could be applied to future urban planning.

CO2 emission induced by international trade brings a plenty of pressure on global warming. Hence, it is important to understand multi-regional CO2 flows that result from global trade and flow variation of different paths. After transforming capital flows into CO2 carbon flows, we built an ecological network model of the global trade system's carbon metabolism. Through ecological network analysis, we identified the direct and indirect effects of temporal CO2 flows in the network. We found that the total integral flow through the network experienced a remarkable increase during the study period. In addition, total import and export flows of four countries, including US, China, Germany and Russia, turned out to be rather big. Direct flow share was usually beyond 50% between two geographical adjacent countries, while that share was below 50% when two countries are far away. This research reveals the importance role of indirect flows and can therefore provide an empirical basis for adjusting and optimizing global trade network.

How the Northeast Revitalization Plan is reshaping the socioeconomic and energy system of this rustbelt region in China remains unclear to date. In this study, an ecological network model based on environmentally extended input-output tables was developed to quantify the changes in the energy metabolic process of Jilin Province under the implementation of the revitalization policy from 2002 to 2012. The results showed that large investments under the revitalization policy triggered substantial increases in embodied energy consumption, from 314.25 Mtce in 2002 to 651.33 Mtce in 2012. The transportation equipment and chemical products sectors achieved evident scale expansion and strengthened their dominant influence on the energy metabolic system, indicated by the increase of both pulling and driving force indicators. However, the construction and service group sectors presented a stagnant energy consumption trend, which suggested that this revitalization process had failed to fuel urbanization and social development. In addition, the dominance of exploitation relationships among the sectors remained unchanged. The robustness index decreased from 0.32 to 0.28 due to the increase in redundancy and the decrease in efficiency in the system, which is an unfavorable trend for sustainable regional development. This research revealed the complicated effect of revitalization efforts on the energy metabolic system of a rustbelt region, which is informative for effective energy policy making as well as revitalization plan adjustment.

A stable energy supply is essential to support urban socioeconomic activities and to ensure urban sustainable development. A quantitative systematic method is necessary to evaluate and improve the security level or urban energy supply. Ecological network analysis (ENA) is thus applied as a useful general analysis tool to systematically simulate, evaluate, and compare energy supply security in different cities. Based on the established ENA model, we evaluated and compared the crude oil supply security for Beijing, Shanghai, and Tianjin from 1997 to 2012, via sustainability, stability, and structure analyses. The results indicate that the overall levels of crude oil supply security for the three cities showed similar features and increase trends during the study period, whereas the key influencing compartments were different. Correspondingly, related suggestions were proposed to improve urban crude oil supply security based on further scenario analysis, including diversifying supply sources, maintaining domestic supply, improving internal crude oil production, increasing consumption within a certain range, and strengthening strategic reserves, with different emphases for different cities. The ENA-based general method provides a new perspective on the systematic evaluation and regulation of urban energy supply security, which can support urban energy policymaking.

The indirect greenhouses gas emission (GHG) embodied in the key urban materials purchased outside the city boundary (Scope 3) are often ignored in traditional city GHG studies, mostly concentrating on the emissions occurring inside the city (Scope 1) and emissions by the purchased electricity out of boundary (Scope 2). And there is little research on the structural analysis of the upstream supply chain for Scope 3. However, a comprehensive urban GHG accounting system is the basis for cities to make appropriate mitigation measures. Identifying the main embodied GHG that dominate the emissions in upstream supply chains can help focus attention on the largest emitters and offer insights into where climate mitigation efforts should be directed. Thus, in this study the Economic Input Output – Life Cycle Assessment (EIO-LCA) approach was used to evaluate the embodied GHG emissions from Scope 3 and to explore the related GHG emissions structure in the upstream supply chain based on the final demand for the key urban materials. And food, water, steel, cement, and fuel were selected as the representative urban materials based on the characteristic of Xiamen City. Our results demonstrate that the total embodied GHG emissions were 13,201.31 kt CO 2 e, very close to the direct GHG emissions from end-use sectors inside Xiamen city. Among the embodied GHG emissions, imported steel, fuels, cement, food, and water accounted for 56, 26, 13, 4, and 1% of the emissions, respectively. The main embodied GHG emissions contributors were found in the upstream supply chain and some related policy implications were presented. Compared to other cities, Xiamen had a relatively low per capita embodied GHG emission, which was 5.24 t CO 2 e lower than eight U.S. cities.

Integrating carbon capture and storage into biomass power plants (BioCCS) can reduce carbon emissions, but its ecological performance associated with natural resources consumption remains unexamined. Taking a typical BioCCS project – the Maowusu biomass direct-fired power plant with the CCS of Spirulina cultivation in Inner Mongolia of China – as a case, this study observed the trade-offs between the carbon reduction benefits and ecological performance of adding CCS to the power plant. Life cycle assessment (LCA) revealed that the combination of CCS avoids 1228 metric tons (MT) of CO 2 emissions annually, while emergy analysis revealed that it deteriorated the ecological performance of the BioCCS system because considerable nonrenewable resources were required by Spirulina cultivation. The BioCCS system is unsustainable in the long run from the ecological point of view. The sensitivity analyses show that there would be no carbon reduction benefits by adding CCS when 60% of designed CO 2 capacity is fixed by Spirulina , and insufficient biomass for electricity generation also affects system performance significantly, which are two main barriers to the BioCCS project. These results indicate that decision-makers should take into account both the carbon reduction benefits and the ecological costs in the development of BioCCS systems.

Emergy analysis can facilitate unified system resources accounting. By combining emergy method with input–output modeling technology, this paper developed an eco-thermodynamic input–output model of the 2007 China economy to account for the sector-specific resource intensities. The results show that the resource intensities for Chinese industry sectors present a distribution with a certain pattern which may vary over three orders of magnitude, measured in terms of resource consumption against economic capital generation. At the scale of the entire economy, the emergy intensities for the resource extraction sectors of non-metallic minerals and metallic ores are the highest. Sectors with the smallest emergy to money ratios are service sectors which rely less on primary natural resources. The sector of coal mining is found to have the largest resource intensity of 6.19E+16 seJ/1E+4 CNY among all sectors, while the sectoral intensity of scrap and waste is only 6.44E+14 seJ/1E+4 CNY, the least one. The insight obtained by juxtaposing resource intensities as well as their structures of industry sectors is useful to identify opportunities for reducing resource intensities that could enable improvements in their ecological sustainability.

Current carbon trading price forecasting research ignores the significance of multiple influence factors, feature selection, preprocessing of the carbon price and its exogenous variables, multi-objective intelligence optimization and kernel-based models in terms of improving prediction validity, which may lead to an undesirable forecasting performance. As a result, a novel hybrid forecasting framework that considers multiple influence factors is developed for carbon price forecasting, which enjoys the merits of some new algorithms and successfully overcomes the challenges of multiple influence factor-based carbon price forecasting. Specifically, both the original carbon price and its exogenous variables benefit from advanced data preprocessing technology. Moreover, feature selection is proposed to determine the optimal feature for modeling the carbon price. Meanwhile, to overcome the limitations of the extreme learning machine and traditional artificial neural network models and obtain desirable, accurate and stable forecasts, the optimal kernel-based extreme learning machine model with good generalizability and stability is developed, grounded in a newly proposed optimizer named the multi-objective chaotic sine cosine algorithm. Experiments, analyses and discussion of the results prove that the developed framework outperforms all compared models which can be as an effective forecasting tool for forecasting and management of the carbon trading market.

Along with rapid urbanization and economic development, urban household consumption has experienced a remarkable change, leading to a corresponding increase in direct and indirect carbon emissions. A systematic accounting of household carbon footprints (HCFs) in Beijing during 2002, 2007, and 2012 using a nested input-output model is presented in this paper. The results indicate that the per capita carbon footprint from urban household consumption in Beijing increased from 3054.05 kg in 2002–4611.12 kg in 2012, of which the indirect emissions were the majority. In terms of consumption structure, food, housing, transportation and communication, and education and entertainment were the four dominant sources contributing to the indirect urban HCFs in Beijing. With respect to the source, local Beijing was the largest contributor for itself materials and service provisions in 2002 and 2007, while the domestic source became the largest contributor in 2012, accounting for 55.08% of the total indirect household carbon emissions. Moreover, we also discovered that families with high income level had much higher HCFs compared to those with low income level. Given these considerations, transitions towards low-carbon household consumption are needed so as to provide equality and sustainability.

Carbon price forecasting plays a vital role in establishing a reasonable and stable carbon market. A number of carbon price forecasting models have been developed to improve the effectiveness of the predictions. However, most of the previous studies failed to focus on the role of choosing the appropriate input features and only aimed to improve the forecasting accuracy. In this paper, a novel hybrid model based on feature selection and a multi-objective optimization algorithm is proposed for carbon price forecasting. More specifically, the main novel contributions of this study are as follows. A two-stage feature selection method is developed to obtain the appropriate input variables to enhance the forecasting ability. In addition, the weighted regularized extreme learning machine is optimized using a multi-objective optimization algorithm, named the multi-objective grasshopper optimization algorithm, which can obtain better forecasting results. To demonstrate the effectiveness of the developed carbon price forecasting model, two daily carbon price datasets that were collected from the China and European Union Emissions Trading Scheme, are used in this study. The results revealed that the mean absolute percentage errors of the proposed model utilizing data from the China and European Union Emissions Trading Scheme are 2.4923% and 0.8418%, respectively, which are lower than those of other compared models. In addition, the variances of the forecasting errors of the developed model are 1.1419 and 0.0038 for the data from the China and European Union Emissions Trading Scheme, respectively. These results reflect the superior forecasting ability of this method compared to other methods. Therefore, the proposed method is more effective than other models in carbon price forecasting.

Approximately half of the global population resides in cities and accounts for over 80% of global carbon emissions. To improve efficiency in carbon emission reduction, and enhance adaptability of cities to climate change, it is essential to undertake accounting of urban carbon cycle. This plays a prominent role in global carbon cycle. An integrated accounting framework for urban carbon cycle was established herein to support uniform accounting and place-specific comparisons, covering natural and artificial carbon cycles, vertical and horizontal carbon flows, carbon storage and fluxes, and carbon inputs and outputs. Guangyuan, a mountainous city in China that was seriously stricken by Wenchuan earthquake in 2008, was chosen as the case to demonstrate the framework and investigate the effects of post-earthquake reconstruction on carbon cycle. The results show: 1) the proposed framework could effectively assess dynamics of carbon cycle under external disturbances, 2) there were abundant vegetation and correspondingly a large natural carbon tank and vertical carbon input, and low industrialization degree and correspondingly small horizontal and vertical carbon outputs in Guangyuan, 3) the post-earthquake reconstruction led to a series of impacts on carbon cycle, from both aspects of amount and structure, and 4) such impacts of post-earthquake construction on artificial carbon cycle were distinct with a tendency toward low-carbon development in Guangyuan. The results were helpful for investigating the effects of natural disturbance and human interference on urban carbon cycle, which in turn could improve the adaptation to climate change.