Search

The coordination of the relationship between new-type urbanization (NTU) and carbon emission reduction has become China's primary strategic goal. However, previous studies on this topic mainly examined the unidirectional impact of NTU on carbon emissions, while disregarding their potential relationship. This study establishes an evaluation system for measuring NTU and explores the bidirectional impact between NTU and total carbon emissions (TCE), per capita carbon emissions (PCE), and carbon emission intensity (CEI). The Yangtze River Economic Belt of China is selected as the study area, and the period from 2005 to 2019 is studied. The results show that: (1) The NTU levels in the upstream, midstream, and downstream regions of the Yangtze River increase from 0.148, 0.208, and 0.365 in 2005 to 0.465, 0.503, and 0.675 in 2019, suggesting that NTU levels tend to be balanced in these three reaches. (2) A positive bidirectional impact is found between NTU and TCE, as well as PCE in midstream and upstream regions, whereas in downstream regions, only a positive unidirectional effect of NTU on TCE and PCE is found. (3) Specifically, TCE plays the most significant role in promoting NTU in upstream regions, while NTU exerts the greatest pulling force on TCE and PCE in midstream regions. (4) Unlike the positive impact between NTU and TCE or PCE, there is a significant two-way inhibitory effect between NTU and CEI. (5) A three-step carbon emission reduction law is found in the process of NTU, where NTU towards low carbon development will experience NTU inhibits CEI, PCE, and TCE in sequential order. These findings provide an important reference for promoting a harmonious relationship between NTU and carbon emission reduction, helping governments formulate reasonable measures to achieve high-quality urban development and carbon emission reduction goals. • Bidirectional impact between new-type urbanization and carbon emissions of the Yangtze River Economic Belt is examined. • New-type urbanization positively affects total carbon emissions and per capita carbon emissions. • Negative bidirectional impact between new-type urbanization and carbon emission intensity is observed. • Regional heterogeneity of the impact between new-type urbanization and carbon emissions is found. [ABSTRACT FROM AUTHOR]

Although China's western cities have developed rapidly in recent years, their ecosystems remain fragile. The conflict between urban development and environmental protection must be resolved to achieve the goal of sustainability. This study examines Xi'an as a case study of a megacity in Western China, calculating the city development index (CDI) and per capita ecological footprint (ef) from 2005 to 2016 to comprehensively calculate Xi'an's eco-efficiency. The E-C elasticity coefficient, which refers to the ratio of the rate of change in CDI to the rate of change in ef , is proposed to characterize the degree to which urban development responds to changes in the ef. The CDI and ef both showed an upward trend from 2005 to 2016. Xi'an's environmental efficiency has grown rapidly, and resource efficiency is the main factor driving its fluctuations in eco-efficiency. The energy consumption and pollution emissions are highly correlated with the CDI, and they are the main factors affecting the development of Xi'an. The relationship between CDI and ef is consistent with the law of diminishing marginal utility, the level of urban development rises and then decreases with the increase in ef. • City development index is significantly related to ecological footprint. • Main factors affecting city development are energy consumption and pollution emission. • Resource efficiency is the main driver of eco-efficiency change in Xi'an. • Development of Xi'an rises and then falls with the increase in ecological footprint. • E-C elasticity coefficient was proposed to illustrate the process of city development. [ABSTRACT FROM AUTHOR]

Numerous environmental problems have been seen due to the "high energy consumption, high pollution, high emissions" economic model in the Beijing-Tianjin-Hebei urban agglomeration (BTHUA). The coupling coordination degree model is applied to provide a coordination of urbanization and ecological environment composite system (CUECS) value while a geographic detector is applied to explore the dominant factors controlling it. This study reached the following conclusions. (1)The CUECS types are mainly low coordination, but which generally exhibit positive evolutionary trend. The change trends can be characterized as urbanization lags followed by system equilibrium followed by ecological environmental lags. (2)The CUECS conforms to a core-edge distributional pattern that comprises plain high mountain low, inland high coastal low. Industrialization played a key role in the development of BTHUA, the landform type was the important factor controlling CUECS. (3) Social consumer goods, gross domestic product, the disposable income of urban residents (all per capita) are the core factors controlling CUECS within different spatial units. Urbanization rate, per capita social consumer goods, the proportion of tertiary industrial population are the core factors controlling CUECS during different urbanization development stages. (4)The relative impacts of urbanization and ecological environmental subsystems on CUECS are (in decreasing order of importance) population urbanization, economic urbanization, social urbanization, ecological environment subsystem. Therefore, green urbanization remains the primary path for sustainable development within the urban agglomeration. It is unsuitable for rapid urbanization development model in the mountainous areas that encapsulate ecological and environmental security as their main functions, so the government urgently needs to amend its 'one size fits all' policy system. • Coordination of urbanization and ecological environment composite system (CUECS) shows core-edge pattern with plain high mountain low, inland high coastal low. • Consumption, gross domestic product, income are core factors controlling CUECS within different spatial units. • Urbanization rate, consumption, service industry are core factors controlling CUECS during different urbanization stages. • Relative impacts on CUECS sort as population urbanization, economic urbanization, social urbanization, ecological environment subsystem. • Green urbanization remains primary path for sustainable development of urban agglomeration. [ABSTRACT FROM AUTHOR]

Abstract Background China and other developing countries in Asia follow similar economic growth patterns described by the flying geese (FG) model, which explains the "catching-up" process of industrialization in latecomer economies. Japan, newly industrialized economies, and China have followed this path, with similar economic development trajectories. Based on the FG model, we postulated a "flying S" hypothesis stating that if a country is located within an FG region and its energy matrix is relatively constant, its per capita CO 2 emission curve will mirror that of "leading geese" countries in the same FG group. Method Historical CO 2 emissions data were obtained from literature review and national reports and were calculated using bottom-up methods. A sigmoid-shaped, non-linear mixed effect model was applied to examine ex post data with 1000 simulated predictions to construct 95% empirical bands from these fits. By multiplying by estimated population, we predicted total emissions of selected FG countries. Results Per capita CO 2 emissions from the same FG group mirror each other, especially among second and third industrial sectors. We estimated an annual 18,252.24 million tons of CO 2 emissions (MtCO 2) (95% CI = 9458.88–23,972.88) in China and 8281.76 MtCO2 (95% CI = 2765.68–14,959.12) in India in 2030. Conclusion This study bridges the macroeconomic FG paradigm to study climate change and proposes a "flying S" hypothesis to predict greenhouse gas emissions in East Asia. By applying our theory to empirical data, we provide an alternative framework to predict CO 2 emissions in 2030 and beyond. Highlights • Per capita CO 2 emissions from developing countries in Asia follow economic growth patterns, described as flying geese model. • Per capita CO 2 emissions trajectory in one country can forecast the emissions in another, under two assumptions. • Our flying-S model provides business-as-usual scenario, predicting counterfactual CO 2 emissions without any intervention. • In 2030, we estimated more than 18 billion tons of CO 2 (BtCO 2) emission in China, and 8 BtCO 2 in India. [ABSTRACT FROM AUTHOR]

With the increasing affluence, the differences in CO 2 emission between urban and rural residential sectors are remarkable and show an increasing trend. In case of China, residential sector accounts for a substantial share of the national CO 2 emission, bringing greater pressure to achieve the goal of carbon peak. Analyzing the emission inequality trend and its drivers is essential for formulating effective CO 2 emission reduction policies. However, the existing literature lacks relevant analysis from the viewpoint of urban-rural disparity. Hence, this study decomposes the CO 2 emission inequality of China's urban and rural residential consumption into four factors by combining the Theil index and Kaya decomposition. The results suggest that, in 2005–2020, the per capita CO 2 emission of rural residential consumption increased to a higher extent than those of urban households, with large differences in spatial distribution. Decomposition of the per capita CO 2 emission inequality for residential sector shows that the primary source is the inequality within the groups, mainly from the urban intra-group inequality. Based on the static decomposition, energy intensity appears as the main factor of urban–rural inequality. The dynamic decomposition shows that there have been differences in the factors of the change in the Theil index between urban and rural areas across sub-periods. • CO 2 emission inequality is discussed based on urban-rural differences. • A Kaya - Theil decomposition is developed. • The case of China is considered via static and dynamic decomposition. • The evolution and drivers of CO 2 emission inequality are explored. [ABSTRACT FROM AUTHOR]

Household inputs and consumption play important roles in driving carbon emissions in China. However, existing studies have mainly studied consumption-based household carbon emissions in specific years to highlight consumption guidance and management, and little attention has been given to income-based accounting and policy-making focused on primary input behaviors and product allocation behaviors. In the quest for more coordinated and efficient mitigation strategies, we applied input–output analysis (IOA) combined with the biproportional scaling method (RAS) to obtain both income- and consumption-based annual accounting of rural and urban household carbon emissions from 2010 to 2017 and then used structural decomposition analysis (SDA) to determine key driving factors and sectors. Our results revealed that the proportions of income-based household emissions in gross emissions were higher than that of consumption-based household emissions. In terms of driving factors, per capita income/consumption contributed the largest increase in household emissions for most of the period, and population changes also showed a weak positive effect. However, intermediate input/output structure and carbon emission intensity were the main offsetting factors for household emissions. Compared with the consumption-based results, the income-based results can identify some new critical sectors that lead to household emission changes. Furthermore, the discrepant results for rural and urban household carbon emissions from both income and consumption perspectives suggest that differentiated measures of rural and urban households in key sectors are necessary. Finally, we propose industrial chain adjustment strategies and household input and consumption behavior recommendations in the context of urbanization. • The share of income-based household carbon emissions was higher than that of consumption. • Income- and consumption-based IOA-SDA are built to investigate the factors in changing household emissions. • Compared with consumption-based, income-based results identify new critical sectors for household emission mitigation. [ABSTRACT FROM AUTHOR]

Reactive nitrogen (Nr) has far-reaching advantages and disadvantages on human beings. Nitrogen footprint (NF) is a tool to quantify the use of Nr in the environment. Food nitrogen footprint (FNF) accounts for the largest proportion of the total NF, and the differences between provinces in China exist objectively. In order to explore the spatial correlation and socio-economic driving factors of China's FNF, this paper uses N-calculator tool to calculate the FNF of 30 provinces in China from 2000 to 2018, and uses exploratory spatial data to analyze the spatial correlation and changes of provincial FNF, The driving factors and spatial effects of FNF change in the province were analyzed by using spatial Durbin panel model and spatial regression partial differential method. The results showed that: (1) There is a significant and stable positive spatial dependence and heterogeneity in the FNF among provinces; (2) The direct effect factors of promoting the growth of FNF in the province are urban household Engel coefficient, per capita disposable income of rural residents and rural household Engel coefficient. The main factors of restraining the growth of FNF in the province are wastewater discharge per unit GDP and per capita GDP; (3) the spillover effect is mainly manifested as the negative effect of the increase of urban household Engel coefficient on neighboring provinces, and the spillover effect of per capita disposable income of urban residents and nitrogen fertilizer application rate per unit grain yield on the growth of FNF of neighboring provinces is significant. From the policy level, it is necessary to guide healthy and scientific eating habits, reduce the proportion of meat and fish in the diet structure, reduce the nitrogen fertilizer application per unit grain yield, and improve the efficiency of chemical fertilizer utilization. When formulating relevant policies, government departments should give consideration to the cooperation between provincial and regional governments. • N-calculator tool is used to calculate the FNF of 30 provinces in China. • Driving factors and spatial effects of FNF change in the province were analyzed. • The spatial Durbin panel model and spatial regression partial differential method are used for the analysis. [ABSTRACT FROM AUTHOR]

Abstract: In 2007, the Chinese State Food and Drug Administration (SFDA) implemented a management system for lot release of all plasma-derived products. Since then, there have been only a few systematic studies of the blood supply, which is a concern when considering the small amount of plasma collected per capita (approximately 3 L/1000 people). As a result, there may be a threat to the safety of the available blood supply. In this study, we examined the characteristics of the supply of Chinese plasma-derived products. We investigated the reports of lot-released biological products derived from all 8 national or regional regulatory authorities in China from 2007 to 2011. The market supply characteristics of Chinese plasma-derived products were analyzed by reviewing the changes in supply varieties, the batches of lot-released plasma-derived products and the actual supply. As a result, the national regulatory authorities can more accurately develop a specific understanding of the production and quality management information provided by Chinese plasma product manufacturers. The implementation of the lot release system further ensures the clinical validity of the plasma-derived products in China and improves the safety of using plasma-derived products. This work provides an assessment of the future Chinese market supply of plasma-derived products and can function as a theoretical basis for the establishment of hemovigilance. [Copyright &y& Elsevier]

China is a world leader on agriculture production; with only 8% of global cropland it feeds 20% of the world's population. However, the increasing production capacity comes with the cost of greenhouse gas (GHG) emissions. As a populous country with the highest GHG emissions in the world, determining how to achieve the dual goals of mitigating climate change and ensuring food security is of great significance for the agricultural sector. This requires assessing the spatial variation in agricultural greenhouse gases (GHGs) and their drivers. In this study, we conduct a spatial assessment of agricultural GHGs at the provincial level in China for the years 1997–2017, and then explore the effects of related factors on GHG emissions using a geographically weighted regression (GWR) model. The results suggest the following. 1) There have always been significant interprovincial variations, whether in the total amount, structure, intensity, or per capita level of agricultural GHG emissions. 2) The directions of the effects of selected factors on GHG intensity fall broadly into three categories: negative effects (urbanization, intensity of agricultural practices, and agricultural structure), positive effects (agricultural investment and cropland endowments), and mixed effects, with factors leading to reductions in some provinces and increases in others (economic level, frequency and intensity of disasters, and the level of mechanization). 3) The magnitude of the effects varies by factor and also by province. The results suggest synergetic province- or state-specific reduction policies in agricultural GHG for China, as well as for other developing and emerging economies. • Systematic assessment of spatial variation in agricultural GHGs is conducted. • Spatial variation of agricultural GHGs is presented in scale and intensity. • The directions and magnitude of the factors' effects vary by province and factor. • Synergetic province-specific policies are critical for developing economies. [ABSTRACT FROM AUTHOR]