From the perspective of economics, high-quality economic development is a concept that is not easy to grasp. How to quantify high-quality development is also a relatively complex topic. The combination of economic growth and development is high-quality economic development, reflecting the core connotation of the modern economic system. It is of great significance to measure the quality of economic development and study its influencing factors. Based on the new development concept of “innovation, coordination, green, openness, and sharing”, this paper establishes an evaluation index system for the high-quality development level of China’s economy. Then, the principal component analysis method was used to measure China’s high-quality economic development level and that of each province in China. Combined with high-quality development’s meaning and essential features, we can correctly judge the regional economy’s specific situation of high-quality development and analyze the results of high-quality economic development from the perspective of time series and spatial evolution. Based on this, we further explore the main factors that influence the level of high-quality economic development. Finally, some feasible suggestions are put forward to improve the quality of China’s economic development and promote the completion of economic transformation. The main contribution of this paper is that the use of principal component analysis can reduce the dimensional and order-of-magnitude differences between the indicators. In this way, we can better measure the high-quality development level of China’s economy, analyze its main influencing factors, and provide new possible paths for China’s economic transformation.
Environmental regulation is an important means of restraining enterprises and protecting the environment. Rationalization of environmental regulatory policies can promote high-quality regional economic development. The optimization and upgrading of the industrial structure has an intermediary effect on the impact of environmental regulations on the high-quality development of the regional economy. After collating and analyzing previous research, this article proposes to classify 30 Chinese provinces into regions with higher than the national average HDI (human development index) and lower than the national average HDI based on the average HDI of Chinese provinces. We explore the mediating effect of industrial structure on environmental regulation and high-quality regional economic development. The model passed the full-sample robustness test and the robustness test with GDP as the replacement variable. The empirical results show that environmental regulations of different intensities have different effects on the quality of regional economic development. The effect of environmental regulations on development quality is mainly mediated through the transformation and upgrading of the industrial structure. Enterprises need reasonable incentives from environmental regulations to transform and upgrade. The mediating effect of the industrial structure on environmental regulations is greater in regions with below-average HDI values than in regions with above-average HDI values, which shows that the industrial structure is the mechanism underlying the effect of environmental regulations on the quality of regional economic development. This result proves that adjusting environmental regulatory policies can effectively promote the upgrading of industrial structure, thereby promoting high-quality regional economic development. Based on this, the article puts forward several policy recommendations.
With the rapid development of China’s economy, China has become the world’s largest carbon emitter. China not only has an obvious growth rate of industrial carbon emissions but also the intensity of agricultural carbon emissions is hovering at a high level. The development of China’s agricultural economy has largely come at the expense of high emissions. Currently, under the background of global warming and difficulty in controlling greenhouse gas emissions, the development of low-carbon agriculture is an important way to realize the harmonious development of the ecological environment and economic growth and to promote the sustainable development of agriculture. The agricultural production efficiency is the main factor affecting the intensity of agricultural carbon emissions. Based on provincial panel data of China from 2010 to 2019, this paper establishes an indicator system and uses the super-efficiency SBM model to measure agricultural production efficiency. The regional agricultural carbon emissions were estimated using carbon-emission-related agricultural production activities. In order to study the nonlinear relationship between agricultural production efficiency and agricultural carbon emission intensity in the narrow sense, this paper uses a threshold regression model with agricultural carbon emissions as the threshold variable. Based on the analysis of China’s agricultural production efficiency and agricultural carbon emissions from 2010 to 2019, an empirical test is conducted through a threshold regression model. The results show an “inverted U-shaped” relationship between agricultural production efficiency and agricultural carbon emission intensity. In areas with high agricultural production efficiency, the improvement of production efficiency can suppress the intensity of agricultural carbon emissions; in areas with low agricultural production efficiency, the improvement of production efficiency increases the intensity of agricultural carbon emissions. Finally, based on the research conclusions, this paper provides feasible suggestions and countermeasures for China’s agricultural carbon emission reduction and improvement of agricultural production efficiency.
With the continued development of the economy, the income gap among Chinese rural households continues to widen. The land system plays a decisive role in developing “agriculture, rural areas and farmers” and land circulation is a factor in the increase in income inequality among farm households. Based on the 2013 China Household Income Project (CHIP), this article used the re-centered influence function (RIF) regression method to empirically test the impact of rural land circulation on the income gap of rural households in China in three regions: the central, eastern and western regions. The quantile regression tested the impact mechanism of income inequality of rural households from the perspective of labor mobility and land circulation. The empirical results showed that land circulation increases the income inequality of rural households. The theoretical mechanism test proved that the dynamic relationship between land circulation and labor mobility increases rural household income. However, this increase has a greater effect on rural households with a high income and a small effect on rural households with a low income, resulting in a further widening of the income gap. Therefore, while increasing the income of rural households through land circulation, the government should also consider income equity. Finally, this article puts forward the policies and opinions on land reform and provides a brief discussion on the future direction of development.
The pace of aging in China is accelerating, from the introduction of family planning to the liberalization of the two-child policy, with a growing proportion of families in the 4–2-1 structure. With filial piety in mind, most adult children will live with their elderly parents and share income and expenditure. Concurrently, due to the inadequacy of the social security system, a heavy supplementary burden of supporting the elderly has been placed on adult children. Based on data from the 2011, 2013, 2015, and 2017 Chinese Social Survey (CSS) of the Chinese Academy of Social Sciences (CASS), this study analyzes the objective factors affecting household elderly support expenditure using the ordinary least squares (OLS) estimation method. It also examines the crowding-out effect of elderly support expenditure on the consumption of different types of households through a panel generalized method of moments (GMM) approach. Finally, the crowding-out effect of elderly support expenditure is discussed in a sub-sample according to the number of households needing to support the elderly aged 60 and above. The empirical results illustrate that there is a crowding-out effect of elderly support expenditure on household consumption, and the magnitude of the crowding-out effect varies for diverse consumption. Our study reveals that the crowding-out effect of elderly support expenditure on core consumption is the largest in a sample with different numbers of elderly persons in families. The empirical results for the sub-sample show that the larger the elderly population, the stronger the crowding-out effect of elderly support expenditure on core consumption and the less pronounced the effect on marginal consumption.
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