China is in a critical period of transforming from the oil and gas era to the renewable energy era. To better understand the process of energy interaction between China and the rest of the world, this study aimed to investigate the basic theoretical cognition of global energy interaction and analyze the pattern and changes of energy interaction between China and the rest of the world with the method of complex networks, multi-region input-output analysis, and other technical methods. The main findings are as follows: (1) Chinas coal-based energy production structure and the huge demand for oil and gas indicate that ensuring overseas oil and gas supply is the most direct logic of energy interaction between China and the rest of the world, and the interaction scopes are mainly concentrated in oil- and gas-rich countries and regions. (2) With the development of renewable energy, the logic of energy interaction of China with the rest of the world has changed from countries and regions rich in oil and gas to countries with global renewable energy development and installation needs for its comparative advantages for manufacturing, which forms a renewable energy trade map that covers all major countries and regions in the world. (3) The overseas energy investment target of China has expanded from a limited number of host countries to Europe, Southeast Asia, and other countries and regions. The investment business is not only limited to the oil and gas field, but also expanded to solar energy, wind energy, hydro-power, and other renewable electricity generation projects. (4) As a global manufacturing and trading power, part of the energy consumed by China is embodied in the global production network and trade network for redistribution. The scope of energy interactions between China and the world will further expand to countries with general commodity trade relations with China, forming the global “energy hub” function. This study can provide a theoretical perspective and decision-making for a deeper understanding of the energy interactions between China and the world, maintaining national energy security, and participating in global energy economic governance.
Guangdong Province, as one of China’s fast-developing regions, an important manufacturing base, and one of the national first round low-carbon pilots, still faces many challenges in controlling its total energy consumption. Coal dominates Guangdong’s energy consumption and remains the major source of CO2. Previous research on factors influencing energy consumption has lacked a systematic analysis both from supply side (factors related to scale, structure, and technologies) and demand side (investment, consumption, and trade). This paper develops the logarithmic mean Divisia index (LMDI) method that focuses on the supply side and the structural decomposition analysis (SDA) method that focuses on the demand side to systematically identify the key factors driving coal consumption in Guangdong. Results are as follows: (1) Supply side analysis indicates that economic growth has always been the most important factor driving coal consumption growth, while energy intensity is the most important constraining factor. Industrial structure and energy structure have different impacts on coal consumption control during different development phases. (2) Demand side analysis indicates that coal is consumed mainly for international exports, inter-provincial exports, fixed capital formation, and urban household. (3) Industries with the fastest coal consumption growth driven by final demand have experienced significant shifts. Increments in industrial sectors were mainly driven by inter-provincial exports and urban household consumption in recent years. (4) Research on energy consumption in subnational regions under China’s new development pattern of “dual circulation” should not only focus on exports in the context of economic globalization but also pay more attention to inter-provincial exports on the background of strengthened interregional connections.
How to simulate land-cover change, driven by climate change and human activity, is not only a hot issue in the field of land-cover research but also in the field of sustainable urbanization. A surface-modeling method of land cover scenario (SSMLC) driven by the coupling of natural and human factors was developed to overcome limitations in existing land-cover models. Based on the climatic scenario data of CMIP6 SSP1-2.6, SSP2-4.5, and SSP5-8.5 released by IPCC in 2020, which combines shared socioeconomic paths (SSPs) with typical concentration paths (RCPs), observation climatic data concerning meteorological stations, the population, GDP, transportation data, land-cover data from 2020, and related policy refences, are used to simulate scenarios of land-cover change in the Jing-Jin-Ji region using SSP1-2.6, SSP2-4.5, and SSP5-8.5 for the years 2040, 2070 and 2100, respectively. The simulation results show that the total accuracy of SSMLC in the Jing-Jin-Ji region attains 93.52%. The change intensity of land cover in the Jing-Jin-Ji region is the highest (plus 3.12% per decade) between 2020 and 2040, gradually decreasing after 2040. Built-up land has the fastest increasing rate (plus 5.07% per decade), and wetland has the fastest decreasing rate (minus 3.10% per decade) between 2020 and 2100. The change intensity of land cover under scenario SSP5-8.5 is the highest among the abovementioned three scenarios in the Jing-Jin-Ji region between 2020 and 2100. The impacts of GDP, population, transportation, and policies on land-cover change are generally greater than those on other land-cover types. The results indicate that the SSMLC method can be used to project the change trend and intensity of land cover under the different scenarios. This will help to optimize the spatial allocation and planning of land cover, and could be used to obtain key data for carrying out eco-environmental conservation measures in the Jing-Jin-Ji region in the future.
Reconstructing long-term changes of forest cover (FC) can provide reliable underlying data for carbon source/sink accounting and simulation of the effects of land use on the climate and environment. Historical maps contain a wealth of forest related information and can provide first-hand data for studying the changes in FC over a long time period. Taking the reconstruction of FC in Taiwan Province from 1910-2010 as the research object, we used map extraction and mining methods to extract forest distribution information from historical forest thematic survey maps, topographic and land cover maps, and reconstructed the spatiotemporal patterns of FC in Taiwan from 1910-2010. The results show that: first, the relative bias of the FC area extracted from the historical maps of Taiwan was below 8%, meaning the FC information contained in maps is highly accurate. Second, the FC of Taiwan has generally declined in the past 100 years. From 1910-2010, the forest area declined from 2.62×106 ha to 2.47×106 ha, with relatively obvious forest reduction having occurred. In stages, the forest area of Taiwan decreased by 26.39×104 ha from 1910 to 1950; and increased by 10.53×104 ha during the period 1950-2010. Forest reduction was obvious during the Japanese occupation period, while forest increase was remarkable during the Kuomintang period. Third, during the study period, the total area of patches maintained as forests was 2.17×106 ha with little change in the overall pattern, and forests were mainly distributed in the mountain regions. The loss of forest mainly occurred in the plains, with expanding forest areas mainly in the mountain regions at high elevations and on steep slopes. Fourth, land clearing for agriculture during the Japanese occupation period has been the important driver of forest reduction in Taiwan over the past century. After retreated to Taiwan, the Kuomintang government introduced effective and remarkable reforms that led to effective restoration of forest vegetation in many areas where forests had previously disappeared.
The world is facing a poverty crisis. Despite the great achievements that have been made in poverty alleviation over the past two decades, the extent of poverty in countries along the Belt and Road is still high. Successful poverty reduction in these countries is crucial to meeting the 2030 SDGs. Improving governance to eradicate poverty is a shortcoming that needs to be addressed urgently in poverty reduction pathways along the Belt and Road. In this study, the Standard Deviational Ellipse method was used to study the spatial trajectory of the poverty gravity center. Results showed that the poverty gravity center moved from east to west by 87.60 km annually, while the poverty population decreased by 1211.14 million along the Belt and Road from 2000 to 2020. In addition, the trajectory of the center has shifted due to the different effects of poverty reduction. Among the countries situated along the Belt and Road, China is the most significant contributor to poverty reduction. Accordingly, this study examined the implications of China’s successful poverty reduction strategies in order to understand how other countries can effectively respond to poverty. Ultimately, we propose that sustainable poverty alleviation development strategies should be established with the objectives of promoting social equity and improving the wellbeing of all people.
It is urgent and important to explore the dynamic evolution in comprehensive transportation green efficiency (CTGE) in the context of green development. We constructed a social development index that reflects the social benefits of transportation services, and incorporated it into the comprehensive transportation efficiency evaluation framework as an expected output. Based on the panel data of 30 regions in China from 2003-2018, the CTGE in China was measured using the slacks-based measure-data envelopment analysis (SBM-DEA) model. Further, the dynamic evolution trends of CTGE were determined using the spatial Markov model and exploratory spatio-temporal data analysis (ESTDA) technique from a spatio-temporal perspective. The results showed that the CTGE shows a U-shaped change trend but with an overall low level and significant regional differences. The state transition of CTGE has a strong spatial dependence, and there exists the phenomenon of “club convergence”. Neighbourhood background has a significant impact on the CTGE transition types, and the spatial spillover effect is pronounced. The CTGE has an obvious positive correlation and spatial agglomeration characteristics. The geometric characteristics of the LISA time path show that the evolution process of local spatial structure and local spatial dependence of China’s CTGE is stable, but the integration of spatial evolution is weak. The spatio-temporal transition results of LISA indicate that the CTGE has obvious transfer inertness and has certain path-dependence and spatial locking characteristics, which will become the major difficulty in improving the CTGE.
Since the launch of China’s reform and opening up policy, the process of urbanization in China has accelerated significantly. With the development of cities, inter-city interactions have become increasingly close, forming urban agglomerations that tend to be integrated. Urban agglomerations are regional spaces with network relationships and hierarchies, and have always been the main units for China to promote urbanization and regional coordinated development. In this paper, we comprehensively consider the network and hierarchical characteristics of an urban agglomeration, while using urban flow to describe the interactions of the inter-city networks and the hierarchical generalized linear model (HGLM) to reveal the hierarchical driving mechanism of the urban agglomeration. By coupling the HGLM with a cellular automata (CA) model, we introduced the HGLM-CA model for the simulation of the spatial expansion of an urban agglomeration, and compared the simulation results with those of the logistic-CA model and the biogeography-based optimization CA (BBO-CA) model. According to the results, we further analyzed the advantages and disadvantages of the proposed HGLM-CA model. We selected the middle reaches of the Yangtze River in China as the research area to conduct this empirical research, and simulated the spatial expansion of the urban agglomeration in 2017 on the basis of urban land-use data from 2007 and 2012. The results indicate that the spatial expansion of the urban agglomeration can be attributed to various driving factors. As a driving factor at the urban level, urban flow promotes the evolution of land use in the urban agglomeration, and also plays an important role in regulating cell-level factors, making the cell-level factors of different cities show different driving effects. The HGLM-CA model is able to obtain a higher simulation accuracy than the logistic-CA model, which indicates that the simulation results for urban agglomeration expansion considering urban flow and hierarchical characteristics are more accurate. When compared with the intelligent algorithm model, i.e., BBO-CA, the HGLM-CA model obtains a lower simulation accuracy, but it can analyze the interaction of the various driving factors from a hierarchical perspective. It also has a strong explanatory effect for the spatial expansion mechanism of urban agglomerations.
Globalization and informatization promote the evolution of urban spatial organization from a hierarchical structure mode to a network structure mode, forming a complex network system. This study considers the coupling of “space of flows” and “spaces of places” as the core and “embeddedness” as the link and a relevant theoretical basis; then we construct a conceptual model of urban networks and explore the internal logic of enterprise networks and city networks. Using the interlocking-affiliate network model and data from China’s top 500 listed companies, this study constructs a directed multi-valued relational matrix between cities in western China from 2005 to 2015. Using social network analysis and the multiple regression of quadratic assignment program model (MRQAP), this study adopts a “top-down” research perspective to analyze the spatio-temporal evolution and growth mechanism of the city network in western China from three nested spatial scales: large regions, intercity agglomerations, and intracity agglomerations. The results show the following: (1) Under the large regional scale, the city network has good symmetry, obvious characteristics of hierarchical diffusion, neighborhood diffusion, and cross-administrative regional connection, presenting the “core-periphery” structural pattern. (2) The network of intercity agglomerations has the characteristics of centralization, stratification, and geographical proximity. (3) The internal network of each urban agglomeration presents a variety of network structure modes, such as dual-core, single-core, and multicore modes. (4) Administrative subordination and economic system proximity have a significant positive impact on the city network in western China. The differences in internet convenience, investment in science and technology, average time distance, and economic development have negative effects on the growth and development of city networks. (5) The preferential attachment is the internal driving force of the city network development.
Land use/cover change (LUCC) is the foundation and frontier for integrating multiple land surface processes. This paper aims to systematically review LUCC research from 1990 to 2018. Based on qualitative and quantitative analyses, we delineated the history of LUCC research and summarized their characteristics and major progress at different stages. We also identified the main challenges and proposed future directions for LUCC research. We found that the number of publications on LUCC research and their total citations grew exponentially. The research foci shifted from the process of LUCC during 1990-2004 to the impact of LUCC during 2005-2013 and then to the sustainability of LUCC from 2014 onwards. Currently, LUCC research is facing theoretical, methodological and practical challenges ranging from integrating the framework of sustainability science, adopting emerging technologies to supporting territorial spatial planning. To move forward, LUCC research should be closely integrated with landscape sustainability science and geodesign and take the leading role in territorial spatial planning to achieve the related Sustainable Development Goals.
Since its inception accessibility has undergone various changes in the way it is defined, measured, and modeled. The paper reviews the recent advancements made in the accessibility measures along with the models used in different applications of accessibility related to land use and transportation. The measures of accessibility are grouped under infrastructure-based, location-based, and person-based measures. The paper finds that although the person-based measures are statistically robust and theoretically sound, they are less preferred than the location-based measure in the accessibility measurement. The review finds recent development such as web based mapping and use of location based data; image mapping through convolutional neural networks; and activity-time constraints modeling in the measures of accessibility. Further, the paper reviews literature from the last five years that have used accessibility to study travel mode choices and household location choices and finds the use of three types of modeling framework - Statistical, Neural Network, and Agent Based models. Based on the literature review, this paper suggests the inclusion of environmental sustainability and gender equity in the accessibility measurement framework and a shift towards model synthesis to enhance the model accuracy and to reduce the present complexities in model building.
