3. New Features of Residential Life and Consumption Structure

This article examines the emerging market countries on their national strategic resources—farmland, fresh water, and fossil energy—which are analyzed from the perspectives of distribution, status of development, and existing issues. The study draws the following conclusions: Emerging market countries have abundant farmland resources yet inadequate per capita resources; because of extensive operation on farmland, grain yield is low, which threatens food security; emerging market countries are saliently short in water resources per capita and face imbalances and low productivity over water use, and their agriculture practices are water-intensive; emerging market countries are growing as global centers for production, consumption, and trade of fossil energy, with a long, coal-dominated consumption structure that has a growing momentum, which subjects them to a greater pressure to reduce carbon emissions; and emerging market countries are inefficient in the use of energy, though they have huge potential for energy conservation and consumption reduction.

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Examining the Driving Factors of the Direct Carbon Emissions of Households in the Ebinur Lake Basin Using the Extended STIRPAT Model

Sustainability ◽

10.3390/su13031339 ◽

2021 ◽

Vol 13 (3) ◽

pp. 1339

Author(s):

Ziyuan Chai ◽

Zibibula Simayi ◽

Zhihan Yang ◽

Shengtian Yang

Keyword(s):

Energy Consumption ◽

Carbon Emissions ◽

Carbon Emission ◽

Driving Factors ◽

Series Data ◽

Lake Basin ◽

Stirpat Model ◽

Consumption Structure ◽

Ebinur Lake ◽

Ebinur Lake Basin

In order to achieve the carbon emission reduction targets in Xinjiang, it has become a necessary condition to study the carbon emission of households in small and medium-sized cities in Xinjiang. This paper studies the direct carbon emissions of households (DCEH) in the Ebinur Lake Basin, and based on the extended STIRPAT model, using the 1987–2017 annual time series data of the Ebinur Lake Basin in Xinjiang to analyze the driving factors. The results indicate that DCEH in the Ebinur Lake Basin during the 31 years from 1987 to 2017 has generally increased and the energy structure of DCEH has undergone tremendous changes. The proportion of coal continues to decline, while the proportion of natural gas, gasoline and diesel is growing rapidly. The main positive driving factors affecting its carbon emissions are urbanization, vehicle ownership and GDP per capita, while the secondary driving factor is residents’ year-end savings. Population, carbon intensity and energy consumption structure have negative effects on carbon emissions, of which energy consumption structure is the main factor. In addition, there is an environmental Kuznets curve between DCEH and economic development, but it has not yet reached the inflection point.

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How Do the Population Structure Changes of China Affect Carbon Emissions? An Empirical Study Based on Ridge Regression Analysis

Sustainability ◽

10.3390/su13063319 ◽

2021 ◽

Vol 13 (6) ◽

pp. 3319

Author(s):

Chulin Pan ◽

Huayi Wang ◽

Hongpeng Guo ◽

Hong Pan

Keyword(s):

Population Structure ◽

Regression Analysis ◽

Population Size ◽

Carbon Emissions ◽

Ridge Regression ◽

Employment Structure ◽

Consumption Structure ◽

Structure Changes ◽

Size Population ◽

Ridge Regression Analysis

This study focuses on the impact of population structure changes on carbon emissions in China from 1995 to 2018. This paper constructs the multiple regression model and uses the ridge regression to analyze the relationship between population structure changes and carbon emissions from four aspects: population size, population age structure, population consumption structure, and population employment structure. The results showed that these four variables all had a significant impact on carbon emissions in China. The ridge regression analysis confirmed that the population size, population age structure, and population employment structure promoted the increase in carbon emissions, and their contribution ratios were 3.316%, 2.468%, 1.280%, respectively. However, the influence of population consumption structure (−0.667%) on carbon emissions was negative. The results showed that the population size had the greatest impact on carbon emissions, which was the main driving factor of carbon emissions in China. Chinese population will bring huge pressure on the environment and resources in the future. Therefore, based on the comprehensive analysis, implementing the one-child policy will help slow down China’s population growth, control the number of populations, optimize the population structure, so as to reduce carbon emissions. In terms of employment structure and consumption structure, we should strengthen policy guidance and market incentives, raising people’s low-carbon awareness, optimizing energy-consumption structure, improving energy efficiency, so as to effectively control China’s carbon emissions.

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Prediction of China’s Energy Consumption Based on Robust Principal Component Analysis and PSO-LSSVM Optimized by the Tabu Search Algorithm

Energies ◽

10.3390/en12010196 ◽

2019 ◽

Vol 12 (1) ◽

pp. 196 ◽

Cited By ~ 3

Author(s):

Lihui Zhang ◽

Riletu Ge ◽

Jianxue Chai

Keyword(s):

Principal Component Analysis ◽

Energy Consumption ◽

Tabu Search ◽

Industrial Structure ◽

Principal Component ◽

Component Analysis ◽

Support Vector ◽

Forecasting Model ◽

Robust Principal Component Analysis ◽

Consumption Structure

China’s energy consumption issues are closely associated with global climate issues, and the scale of energy consumption, peak energy consumption, and consumption investment are all the focus of national attention. In order to forecast the amount of energy consumption of China accurately, this article selected GDP, population, industrial structure and energy consumption structure, energy intensity, total imports and exports, fixed asset investment, energy efficiency, urbanization, the level of consumption, and fixed investment in the energy industry as a preliminary set of factors; Secondly, we corrected the traditional principal component analysis (PCA) algorithm from the perspective of eliminating “bad points” and then judged a “bad spot” sample based on signal reconstruction ideas. Based on the above content, we put forward a robust principal component analysis (RPCA) algorithm and chose the first five principal components as main factors affecting energy consumption, including: GDP, population, industrial structure and energy consumption structure, urbanization; Then, we applied the Tabu search (TS) algorithm to the least square to support vector machine (LSSVM) optimized by the particle swarm optimization (PSO) algorithm to forecast China’s energy consumption. We collected data from 1996 to 2010 as a training set and from 2010 to 2016 as the test set. For easy comparison, the sample data was input into the LSSVM algorithm and the PSO-LSSVM algorithm at the same time. We used statistical indicators including goodness of fit determination coefficient (R2), the root means square error (RMSE), and the mean radial error (MRE) to compare the training results of the three forecasting models, which demonstrated that the proposed TS-PSO-LSSVM forecasting model had higher prediction accuracy, generalization ability, and higher training speed. Finally, the TS-PSO-LSSVM forecasting model was applied to forecast the energy consumption of China from 2017 to 2030. According to predictions, we found that China shows a gradual increase in energy consumption trends from 2017 to 2030 and will breakthrough 6000 million tons in 2030. However, the growth rate is gradually tightening and China’s energy consumption economy will transfer to a state of diminishing returns around 2026, which guides China to put more emphasis on the field of energy investment.

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