Carbon Dioxide Emissions and Their Driving Forces of Land Use Change Based on Economic Contributive Coefficient (ECC) and Ecological Support Coefficient (ESC) in the Lower Yellow River Region (1995–2018)

Abstract

Land use change is the second largest source of greenhouse gas emissions after fossil combustion, which can hurt ecological environment severely. Intensive study on land use carbon emissions is of great significance to alleviate environmental pressure, formulate carbon emission reduction policy, and protect ecological development. The lower Yellow River area is an important area of economic development, grain cultivation, and agricultural production in China. Land use change has significant economic, environmental, and ecological impacts in this region. Deep study of land used carbon emissions and its influencing factors in the lower Yellow River area is not only of great significance to the environmental improvement in the Yellow River basin, but also can provide references for the research of other basins. Based on this, this paper studies the land use carbon emissions of 20 cities in the lower Yellow River area from 1995 to 2018. The results showed that from 1995 to 2018, the land use change was characterized by the decrease of the ecological land and the increase of the built-up land significantly. The overall carbon emission of the lower Yellow River area is increasing, and the built-up land is the main factor that leads to the increase of carbon emission, which can be also proven by the analysis of the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model. The economic contributive coefficient (ECC) and ecological support coefficient (ESC) of carbon emission in the lower Yellow River area show a trend of high in Zhengzhou, Jinan, and Zibo and low in Zhoukou, Shangqiu, and Heze, and there was no significant changes during the study period, which indicates that each city did not achieve the coordinated development of the ecological economy. Finally, analysis results of the STIRPAT model indicated that the area of built-up land had the greatest impact on land use carbon emissions, followed by tertiary industry, whereas per capita gross domestic product (GDP) had the smallest impact. For every 1% increase in the area of built-up land, carbon emissions increased by 1.024%. By contrast, for every 1% increase in the contribution of tertiary industry to the GDP and per capita GDP, carbon emissions decreased by 0.051% and 0.034%, respectively. According to the study, there are still many problems in the coordinated development of economy and ecology in the lower Yellow River area. The lower Yellow River area should control the expansion of built-up land, afforestation, development of technology, reduction of carbon emissions, and promotion of the high-quality development of the Yellow River Basin.

Impacts of Energy Efficiency and Economic Growth on Air Pollutant Emissions: Evidence from Angara–Yenisey Siberia

“…Although most recent STIRPAT applications use the panel data to explore both the spatial and temporal heterogeneity of the pressure of social and economic development on the environment (e.g., [15,28,[56][57][58]), the original works by York, Dietz, and Rosa [27,31] were made using static cross-sectional samples. We use this approach and Formula (3) as a general form of regression due to the lack of available data for some critical variables.…”

Dynamic Changes, Spatiotemporal Differences and Factors Influencing the Urban Eco-Efficiency in the Lower Reaches of the Yellow River

“…Based on the natural division, the lower reaches of the Yellow River, with a length of 785.6 km, can be divided from Taohuayu to Lijin County [ 43 ]. Because the close relationship between regional economic development in the lower reaches of the Yellow River and the integrity of administrative divisions at the prefectural level, the areas covered by the irrigation areas in the lower reaches of the Yellow River are merged into the study area [ 41 , 44 ] (see Figure 1 ).…”

“…Based on previous studies [ 43 ], we used the STIRPAT model in this work to detect the driving factors that affect the urban eco-efficiency of the lower reaches of the Yellow River. It measures the 6 aspects of population agglomeration, wealth, technological progress, industrial structure, openness, and investment intensity.…”

“…The Super-SBM model was used to measure the change characteristics of urban eco-efficiency in the lower reaches of the Yellow River during 2007–2018. Moreover, the STIRPAT model, which is relatively mature with respect to the analysis of the eco-environmental tradeoff and coordination [ 42 , 43 ], was introduced to determine the effect of human factors, such as population, affluence, technology, and urbanization, on urban eco-efficiency. The results support urban development and eco-environmental protection, providing references for research on the sustainable development of the region and cities with similar environmental and economic conditions worldwide.…”

Prediction of the potential geographical distribution of Betula platyphylla Suk. in China under climate change scenarios

“…Global warming has important impacts on community composition and structure, ecosystem function, biodiversity, and species distribution change [ 4 – 7 ]. Since the industrial revolution, built-up land has expanded [ 8 ], and land-use types have changed [ 9 ], and, under the joint action of human activities and natural factors, global warming has progressed [ 10 ]. According to the Fifth Assessment Report of the United Nations Intergovernmental Panel on Climate Change (IPCC), the global average temperature will increase by 0.3–4.8°C by the end of the 21st century (2081–2100) compared with that from 1986–2005 [ 11 ].…”