Spatial-Temporal Characteristics of Agriculture Green Total Factor Productivity in China, 1998-2016: Based on More Sophisticated Calculations of Carbon Emissions

Spatiotemporal dynamic of rice production and its carbon footprint in Hainan, China: Implications for food security and environmental sustainability

Rice (Oryza sativa L.) feeds more than half of the global population and faces the critical issues related to food security and environmental sustainability. This study analyzed double rice production data from 2010 to 2020 to assess its spatiotemporal dynamic in food production and carbon (C) footprint in Hainan province, China. The results revealed a 29.5% reduction in rice planting area, leading to a significantly decreased rice self-sufficiency rate from 38% to 33% from 2010 to 2020. During this period, the carbon footprint per unit area (CFa) for early, late, and double rice showed a fluctuating upward trend ranging from 8.1 to 8.4, 8.9 to 9.2, and 17.0 to 17.4 t CO2-eq ha-1, respectively. The total greenhouse gas (GHG) emissions of rice production decreased to around 2 million t CO2-eq, primarily due to reduced planting area. The C sequestration initially increased before decreasing to 1.2 million t C in 2020 at a temporal scale. Spatially, the northeast and southwest regions exhibited ∼70% of the total GHG emissions and ∼80% of C sequestration. The regional C footprint per unit yield displayed less favorable outcomes, with some areas (e.g., Wenchang and Haikou) experiencing emission hotspots in recent years. Higher yield and smaller CFa for Lingao and Tunchang were observed compared to the average between 2010 and 2020. This study provides insights into the spatiotemporal dynamics of double rice production and GHG emissions in Hainan, offering a scientific reference for regional food security and environmental sustainability.

The impact of climate change and production technology heterogeneity on China's agricultural total factor productivity and production efficiency

Sustainable agricultural production efficiency is important for global food security, environmental conservation, economic development, human Health, and social equity. However, Climate change has had a significant impact on global agricultural productivity. To this end, investigating climate change's effect on agricultural production efficiency is critical for the food security of any particular country or region, and China is not distinct. Further, the influencing factor of agricultural total factor productivity (technology or technical efficiency) and regional heterogeneity in agricultural production technologies of China are worth exploring for sustainable agricultural growth. To this end, this study employed the DEA-Malmquist Productivity Index to gauge the total factor productivity change (TFPC) in 31 provinces and administrative units of China from 2000 to 2021. Additional inputs of climate factors were added to the estimation process to explore the impact of climate change on TFPC for different periods and regions. The meta-frontier analysis estimates the agriculture production technology gap among nine regions of China. Results revealed that climate factors could overestimate China's average total factor agricultural productivity over the study period. Among 8 out of 9 regions in China witnessed the diverse effects of climate factors; however, it positively impacted agricultural TFPC in the Qinghai Tibet Plateau. Sichuan Basin and surrounding regions performed best, ranked top in China with an average growth rate of 22.3 % in TFPC. Decomposing the TFPC into efficiency and technological change, the study found that the influence of climate on technological change is greater than compared to efficiency change. Northeast China Plain and Sichuan Basin and surrounding regions have superior agriculture production technology with a TGR score 1. Mann-Whitney U and Kruskal-Wallis test proved the statistically significant difference among agricultural productivity scores estimated with and without climate factors and production technology gaps among nine regions of China.

The green productivity of broiler production in China: Considering the resource utilization of manure

Resource constraints and environmental challenges have emerged as serious impediments to the sustainable development of China's broiler industry, with potentially adverse consequences. The pursuit of sustainable development in China's broiler industry is predicated on significant reductions in manure and pollutant emissions from broiler farming. This study utilizes the slacks-based model and the global Malmquist-Luenberger index to calculate the green total factor productivity of broiler breeding across various provinces and scales from 2005 to 2020 within a joint production framework of considering undesirable outputs and desirable outputs. Fluctuations in economic distribution of broiler breeding are characterized using the kernel density estimation, and a convergence analysis is performed via absolute and conditional β convergence methods. The results revealed an overall upward trend in China's broiler farming green total factor productivity from 2005 to 2020, corresponding to green total factor productivity in small-, medium-, and large-scale broiler breeding were 1.015, 1.017, and 1.009, respectively. The kernel density curve implies a narrowing trend in the discrepancy of green total factor productivity levels among provinces in broiler breeding of varying scales. For all scales, broiler breeding's green total factor productivity demonstrates considerable conditional and absolute β convergence. Therefore, improving the efficiency of broiler breeding while addressing externalities requires the cultivation of broilers at different scales across diverse regions, coupled with an increased focus on improving the utility efficiency of broiler waste fertilization.

The influences of the advancement of green technology on agricultural CO2 release reduction: A case of Chinese agricultural industry

The development of green technology (GT) may have a vital influence in decreasing carbon releases, and the linkage between the advancement of GT and CO2 releases in China's agricultural industry has not attracted enough attention. The main objectives of this study are to assess the influence of agricultural green technology advancement on efficiency enhancement, release control capabilities, agricultural energy structure, and agriculture industrial structure. This article decomposes the advancement of green technology (AGTP) in the agricultural industry in China into resource-saving green technology advancement (AEGTP) and emission reduction green technology advancement (ACGTP). At the same time, to evaluate the intermediary impact of green technology advancement, a two-step econometric model and an intermediary impact model were utilized to evaluate the panel data of 30 provinces in China from 1998 to 2018. The role of AGTP (including ACGTP and AEGTP) and CO2 release concentration has also been explored critically. The results show that (i) under the two-step measurement method, AGTP has substantial favorable impacts on agricultural energy efficiency (EF) and possesses a negative impact on agriculture industrial structure (PS) and agricultural energy structure (ES). Agricultural energy efficiency (EF) and agriculture industrial structure (PS) under AGTP will reduce CO2 release concentration, but the path of agricultural energy structure (ES) will increase CO2 release concentration. (ii) At the national level, AGTP has an immediate unfavorable influence on CO2 releases. After introducing the intermediary variables, the intermediary impact of AGTP on CO2 releases through agricultural energy efficiency (EF), agriculture industrial structure (PS), and agricultural energy structure (ES) is also significantly negative, and the direct impacts of each variable are higher than the intermediary impact. (iii) In terms of different zones, the direct impacts of AGTP are all significant. The order of significance of the direct impacts of different zones is west to central and central to eastern. The overall significance ranking of the mediating impact is ACGTP > AEGTP > AGTP, and the significance ranking of each index is ES > EF > PS. Finally, this article puts forward some policy recommendations to reduce CO2 releases.

Does farmland inflow improve the green total factor productivity of farmers in China? An empirical analysis based on a propensity score matching method

Promoting the "double security" of agricultural economy and ecology is the key to the agricultural modernization strategy, and the large-scale development of agriculture is an essential way for modern agriculture. Based on the micro-survey of 697 corn growers from August to September 2020 in China, the super-efficiency SBM model was used to calculate farmers' green total factor productivity. We further used the propensity score matching method to identify the impact of farmland inflow on farmers' green total factor productivity and dissect the internal mechanism. The study found that: firstly, compared with the non-inflowed households, the green total factor productivity of the inflowed households increased by 14.66%; secondly, farmland inflow can significantly improve farmers' green total factor productivity through the marginal output leveling effect, transaction benefit effect, and technology adoption effect; thirdly, the influence of farmland inflow on the green total factor productivity of farmers has heterogeneity in age, identity, and geographical location. Therefore, governments should establish a differentiated farmland inflow mechanism according to local conditions, enhance factor mobility and soil fertility monitoring capabilities, and drive a "win-win" between economic development and ecological protection.

The Effect of Farmland Transfer on Agricultural Green Total Factor Productivity: Evidence from Rural China

Exploring the effect and mechanism of farmland transfer on agricultural green total factor productivity (AGTFP) in China is of great significance for exerting the effectiveness of China's farmland transfer policy and promoting green agricultural development. Based on panel data from 30 provinces from 2005 to 2020, this paper applies a two-way fixed effects model to analyze the impact of farmland transfer on AGTFP, and the mechanism of farmland transfer on AGTFP is also investigated. We find that farmland transfer has a significant and sound promoting effect on AGTFP, with respect to multiple robustness checks; there is heterogeneity regarding the impact of farmland transfer on AGTFP in terms of food functions, and farmland transfer can promote regional AGTFP through nonagricultural labor transfer and agricultural technology utilization. When considering the fact that farmland transfer has increased China's AGTFP, the Chinese government should continue to adhere to the farmland transfer policy, accelerate nonagricultural labor transfer, improve the level of agricultural technology utilization, and ultimately promote green agricultural development.

Impact of the eco-efficiency of food production on the water-land-food system coordination in China: A discussion of the moderation effect of environmental regulation

The coordination of the water-land-food ("WLF") system is an essential guarantee for ecologically sustainable food production. Based on the perspective of symbiosis theory, we explore practical strategies for enhancing WLF system coordination in China. First, we applied the entropy TOPSIS method to measure WLF system coordination. Second, we used the global-Malmquist-Luenberger ("GML") index to calculate the eco-efficiency of food production. Third, we used the panel Tobit model to empirically explore the improvement path of WLF system coordination and test the moderating role of environmental regulation. Our research has led to the following five conclusions: (i) From 2003 to 2019, the coordination level of the WLF system in all regions of China showed a decreasing trend followed by an increasing trend, with the highest in the Northeast (0.380). The eco-efficiency of food production showed an upward trend in general, with the middle and lower reaches of the Yangtze River (2.101) and the northeastern region (1.888) at a higher level nationwide; (ii) The eco-efficiency of food production does effectively promote WLF system coordination, but with a specific time lag; (iii) The regression results of northern China and major grain-producing areas are consistent with the whole sample. However, the eco-efficiency of food production and its lagging term in southern regions and nonmajor grain-producing regions cannot effectively promote WLF system coordination; (iv) According to the quantile regression results, the promotion of eco-efficiency in food production is more pronounced in regions with higher WLF system coordination (at the 50 %-90 % quantile); and (v) Environmental regulation has a positive moderating effect on the ecological efficiency of food production on the coherence of the WLF system. Moreover, the regression results of grouping moderation show that environmental regulations can play a more vital, positive moderating role in the lower regions compared with higher WLF system coordination regions. Our research innovatively explores the influencing factors of WLF System Coordination. Our research also provides a reference for the formulation of food ecological planting strategies and government environmental regulation policies.

How Does Agricultural Mechanization Service Affect Agricultural Green Transformation in China?

Agricultural mechanization service (AMS) is a critical path to achieving agricultural green transformation with smallholders as the mainstay of agricultural production. Based on the panel data of 30 Chinese provinces from 2011 to 2020, this paper measures the AGTFP using the Super-SBM model and examines the effects of different AMS supply agents on AGTFP and spatial spillover effects through the spatial Durbin model. The main conclusions are as follows: First, China's AGTFP showed a stable growth trend, with the mean value increasing from 0.1990 in 2011 to 0.5590 in 2020. Second, the specialization (SPO) and large-scale (LSO) of AMS supply organizations have significantly positive effect on the AGTFP of the local province. However, SPO has a significantly positive effect on the AGTFP of the neighboring provinces, while LSO has the opposite effect. Third, the specialization of AMS supply individuals (SPI) has significantly negative effect on the AGTFP of the local province. In contrast, the large-scale AMS supply individuals (LSI) has the opposite effect. Furthermore, the spatial spillover effects of both are insignificant. Fourth, the spatial spillover effect of AGTFP shows asymmetry among different regions and indicates that AMS resources flow from non-main grain production and economically developed regions to main grain production and less developed regions. These findings provide helpful policy references for constructing and improving the agricultural mechanization service system and realizing the agricultural green transformation in economies as the mainstay of agricultural production.

Does Agricultural Credit Input Promote Agricultural Green Total Factor Productivity? Evidence from Spatial Panel Data of 30 Provinces in China

Improving agricultural green total factor productivity is crucial to promoting high-quality agricultural development. This paper selects the panel data of 30 provinces in China from 2009 to 2020 and uses the super-efficiency SBM model with undesirable outputs to measure the agricultural green total factor productivity of all regions in China. On this basis, this paper uses the panel data fixed-effect model and spatial Durbin model to empirically discuss the impact of agricultural credit input on agricultural green total factor productivity and its spatial spillover effect. The main conclusions are as follows: First, from 2009 to 2020, the average values of agricultural green total factor productivity in national, eastern, central, and western regions are 0.8909, 0.9977, 0.9231, and 0.8068, respectively, and the agricultural green total factor productivity needs to be further improved. Second, the agricultural green total factor productivity presents a significant and positive spatial correlation, and the spatial distribution of agricultural green total factor productivity is not random and irregular. Third, agricultural credit input can significantly promote agricultural green total factor productivity in the local region, but it hinders the improvement of agricultural green total factor productivity in the adjacent regions. Fourth, the impact of agricultural credit input on the agricultural green total factor productivity and its spillover effect has a significant regional heterogeneity. This paper believes that paying attention to the spatial spillover effect of agricultural total factor productivity, optimizing the structure and scale of agricultural credit input, and formulating reasonable agricultural credit policies can improve agricultural green total factor productivity.

Green-Biased Technical Change and Its Influencing Factors of Agriculture Industry: Empirical Evidence at the Provincial Level in China

The continued expansion of agriculture must contend with the dual pressures of changing factor endowment structure and constrained resources and environments. The main purpose of this paper is to provide feasible ideas for high-quality agricultural development in the transition period through the research on the green-biased technical change in Chinese agriculture. This paper selects China's provincial panel data of the agriculture industry from 1997 to 2017, combining the DEA-SBM model and Malmquist-Luenberger index decomposition method to calculate the green-biased technical change (BTC) index; second, the influence mechanism of BTC is empirically investigated by using the panel data regression analysis approach. The results show that: (1) in China's agriculture industry, BTC is the driving force behind long-term and steady improvement of technological advancement. Specifically, input-biased technical change (IBTC) has a substantial enhancing effect on agricultural green total factor productivity (GTFP), whereas output-biased technical change (OBTC) has a certain inhibiting effect. (2) On the whole, the tendency of capital substituting for labor and land is very evident, whereas the biased advantage of desirable output is not particularly prominent. (3) The BTC index in Chinese agriculture varies regionally. The eastern region has the highest IBTC index but the lowest OBTC index. (4) The degree of marketization, urbanization, capital deepening, financial support for agriculture, and other factors have a promoting effect on IBTC, whereas most of them have a restraining effect on OBTC. There is evident regional heterogeneity in the effect of environmental regulation intensity on BTC. The following are the primary contributions of this paper: based on national conditions in China, this paper empirically explores the changes and internal rules of green-biased technical change in China's agriculture industry from various regional viewpoints. It provides an empirical foundation for the regional diversification of agricultural green transformation.

Internet technology and green productivity in agriculture

The high-quality development of agriculture requires not only sustainable growth of agricultural productivity but also green agricultural production. Internet technology has played an essential role in agricultural production and marketing in China over the past decades. This paper estimates provincial agricultural green growth in China from 1997 to 2019 and decomposes it into technological progress (TP) and efficiency changes (EC) based on the Luenberger productivity indicator method. Then an econometric model is employed to analyze the impact of the Internet on the growth of agricultural green productivity and each sub-component, and moderating role of farmer education in such effect. The empirical results indicated that annual average growth rate of agricultural green productivity in China is 1.33% from 1997 to 2019, and technological progress dominates its growth. The development of Internet technology has a significant positive impact on agricultural green productivity and its decomposition. Farmer education has strengthened the effect of Internet technology on agricultural green productivity and its decomposition TP and EC.

A nonseparable undesirable output modified three-stage data envelopment analysis application for evaluation of agricultural green total factor productivity in China

Agricultural sector is the basic industry providing food for human and supporting the development of national economy. The agricultural green total factor productivity (AGTFP) plays a significant role in coordinating agriculture sustainable development and pollution abatement. We employ a nonseparable undesirable output modified three-stage data envelopment analysis to evaluate the AGTFP of China's 30 provinces from 2000- 2018. We construct a more comprehensive AGTFP measurement indicator system, including seven separable inputs and three non-separable inputs and one separable good output and two non-separable bad outputs. The empirical results demonstrate that it is necessary to run a stochastic frontier analysis to eliminate the influence of random error and external environment. We get a more scientific and accurate results. The real AGTFP experiences an increase trend during the sample. From the spatial perspective, there is an obvious regional difference across the country. Efficiency decomposition indicates that the source of inefficiency is mainly from two undesirable outputs. Therefore, policy implications are put forward.

Does Cleaner Household Energy Promote Agricultural Green Production? Evidence from China

Cleaner household energy for agricultural green production can significantly alleviate energy poverty and food security, thus contributing to global sustainable development. Using survey micro-data collected from Sichuan Province, the ordered probit model, OLS model, and instrumental variables approach were applied for empirical analysis. The results show that: (1) cleaner household energy significantly enhances farmer's agricultural green production awareness and improves agricultural green production levels, which is still significant after treating endogenous issues with the conditional mixing process estimation method and 2SLS model; (2) health plays a partially mediating effect of cleaner household energy on agricultural green production awareness and agricultural green production levels; (3) environmental protection awareness and digital literacy have a moderating effect and reinforce the positive impact of cleaner household energy on agricultural green production awareness and agricultural green production levels. This research suggests that governments can enhance the impact of cleaner household energy on agricultural green production through price and subsidy mechanisms.

The Spatial and Temporal Evolution of the Coordination Degree in Regard to Farmland Transfer and Cultivated Land Green Utilization Efficiency in China

In many parts of the world, the shortage of cultivated land and the food crisis are worsening on a continued basis. Hence, the central and local governments of the PRC have successively issued various related policies to encourage the practice of farmland transfer, promote the eco-friendly utilization of cultivated land, and ameliorate the efficiency of cultivated land utilization. Under the context of large-scale farmland transfer and rural revitalization strategy in China, it is significant to ensure agricultural sustainability through the coordination of farmland transfer and the amelioration of cultivated land green utilization efficiency (CLGUE). In the present study, 30 Chinese provinces were taken as the research object, with the super-efficient SBM model, the coupling coordination degree model and the spatial analysis model applied in combination. Based on the measurement of CLGUE, a thorough analysis was conducted to explore the evolution of coordination degree in regard to farmland transfer and CLGUE in China from both spatial and temporal perspectives. The conclusions drawn from this study are as follows. Firstly, the overall CLGUE exhibited an upward tendency in the PRC, from 0.440 in 2005 to 0.913 in 2019, with a yearly growth rate of 5.47% on average. However, there were significant spatial disparities in CLGUE between different regions and provinces. Secondly, there was a steady increasing trend shown by the level of coordination degree regarding farmland transfer and CLGUE across China. Further, due to the variation in natural and economic conditions, there were significant spatial-temporal disparities in the coordination degree among these 30 provinces. Lastly, there were obvious spatial aggregation patterns at the provincial level regarding the coordination degree in farmland transfer and CLGUE across China. However, there was a declining trend in the level of spatial aggregation patterns for coordination degree.

Indicators for Evaluating High-Quality Agricultural Development: Empirical Study from Yangtze River Economic Belt, China

Agriculture is the foundation of the national economy, and achieving high-quality agricultural development is an important support for strong economic development in the post-pandemic era. Based on the new development philosophy of the Chinese government, this study constructs an evaluation framework of "innovation-coordination-green-openness-sharing" for high-quality agricultural development, and quantitatively assesses the level of high-quality agricultural development in China's Yangtze River Economic Belt with a systematic integration model, and explores the spatial evolution characteristics and obstacles of the level of high-quality agricultural development in Yangtze River Economic Belt. It reveals that the level of high-quality agricultural development in the Yangtze River Economic Belt shows a fluctuating upward trend in general, but there is variability among regions. The green dimension has the fastest development rate, followed by innovation and sharing. In terms of spatial characteristics, it gradually shows a pattern dominated by high levels and shows the characteristics of agglomeration, but the spatial correlation is not high. In terms of obstacle factors, openness and coordination are the main obstacle factors. Considering the different agricultural development models, it is suggested that international cooperation, new agricultural cooperation, and differentiated policies can be considered to promote high-quality agricultural development. This study provides a more complete evaluation framework for government policy-making authorities to measure the level of regional agricultural development and help regional agriculture achieve sustainable development at a higher quality level.

Spatial Distribution and Convergence of Agricultural Green Total Factor Productivity in China

The power source, spatial-temporal differentiation and convergence of the growth rate of green total factor productivity in China’s agriculture were analyzed. The Malmquist index was used to measure the growth rate, and the spatial-temporal convergence was tested by σ convergence, absolute β convergence, conditional β convergence and dynamic spatial convergence. The study drew conclusions that the impetus for the intensive growth of green agriculture was insufficient, and the driving force for the growth of agricultural green total factor productivity (AGTFP) in the eastern, western and central region was green technology progress. In addition, AGTFP did not have an absolute σ convergence trend. Dynamic spatial absolute β and conditional β convergence indicated that regional differences were not completely related to regional endowment conditions, and regional green agricultural production was unbalanced. This study provides an important support for regional green development in China’s agriculture.

Realizing the Improvement of Green Total Factor Productivity of the Marine Economy-New Evidence from China's Coastal Areas

Paying attention to the mechanisms of the GTFP of the marine economy and designing a scientific and reasonable optimization path are the keys to achieving a "win-win" balance between environmental protection and high-quality marine development. Therefore, this paper considers the rigid constraints of resources and negative environmental effects to construct a multi-factor evaluation model of the GTFP of the marine economy including capital, labor, and resources to expand the evaluation method system for the sustainable development of the marine economy. On this basis, this paper determines the influencing factors of the GTFP of China's marine economy, qualitatively analyzes the mechanism of each influencing factor on the GTFP of the marine economy, uses multi-dimensional data of coastal areas, quantitatively analyzes the direct and indirect effects of the factors that influence the GTFP, and proposes practical optimization paths and safeguarding measures, which provide a decision-making reference for the implementation of China's marine development strategy. The results showed that the GTFP of China's marine economy was in a state of improvement and increased from 0.9878 in 2006 to 1.2789 in 2018. The direct effects of environmental regulations have a negative and significant impact on GTFP, whereas economic development, human capital, and technological innovations have a positive and significant impact on GTFP. In addition, environmental regulations have an "inclined N" double-threshold effect on GTFP. The impact of environmental regulations on the GTFP of the marine economy depends on the intensity of the environmental regulations, as different intensities of environmental regulations have different dominant levels of the "innovation compensation effect" and "offset effect" that affect the GTFP of the marine economy.

Measuring China's agricultural green total factor productivity and its drivers during 1998-2019

Improving agricultural green total factor productivity (AGTFP) is essential to China's agricultural sustainable development. Although several studies have focused on China's AGTFP, its measurement and drivers are not fully investigated yet. More specifically, the published research examining the drivers of China's AGTFP at both the production and factor levels is still scarce. To fill this gap, this study constructs two different data envelopment analysis models combined with green Luenberger productivity indicator (GLPI), the biennial weight modified Russell model and the biennial bounded adjusted model, to measure China's AGTFP as well as check the robustness. We further decompose the AGTFP growth at both production and factor levels to investigate its drivers. The main findings are as follows. First, during 1998-2019, the central region with its GLPI at 0.0377 had the largest AGTFP growth, followed by the western (0.0281) and eastern regions (0.0254). Second, in terms of production-decomposition, technical progress was crucial driver to AGTFP growth, energy conservation and emission reduction (ECER) and market performance. Third, in terms of factors-decomposition, the contributions of these factors to the AGTFP growth were positive and the contribution rates ranged from 1.01% (pesticide) to 38.51% (agricultural carbon emissions). Additionally, ECER performance was the primary driver of AGTFP, accounting for about 51.35% of the growth. Finally, according to the decompositions, Porter effect was discovered in China's agricultural sector. ECER drove China's agriculture to achieve win-win development between the environment and economic production.

Spatial-temporal evolution of the relationship between agricultural material inputs and agricultural greenhouse gas emissions: experience from China 2003-2018

Agricultural materials input (fertilizer and pesticide, etc.), together with straw burning, rice planting, and livestock breeding, constitute the sources of agricultural greenhouse gas (GHG) emissions. However, most related studies have discussed the total amount of agricultural GHG emissions or the role of straw burning and rice planting in agricultural GHG emissions and few studies on agricultural GHG emissions from Agricultural materials. Based on the data of 31 provinces in China from 2003 to 2018, this paper explored the evolution process of agricultural GHG emissions from Agricultural materials. Our research turned up some interesting findings. For example, firstly, Agricultural materials play an increasingly important role in agricultural GHG emissions. Agricultural GHG emissions due to Agricultural materials account for an increasing proportion of the total agricultural GHG emissions. Secondly, there are regional differences in the evolution trend of agricultural GHG emissions caused by agricultural materials. Especially after the urbanization rate broke through the critical line of 50% around 2010 in China.

Does Digital Inclusive Finance Effectively Promote Agricultural Green Development?-A Case Study of China

Agricultural green development is increasingly being discussed in sustainable development. This paper constructs agricultural green development from four dimensions: resource savings, environmental protection, ecological conservation, and quality industrialization. We apply the entropy-weighted Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method to measure agricultural green development and employ a panel dataset of provinces in China from 2011-2019. Then, the dynamic spatial Durbin model is adopted to estimate the spatial effect of digital inclusive finance on agricultural green development. The main findings are as follows: (1) digital inclusive finance effectively promotes agricultural green development, and the promotional effect shows temporary and spatial spillover; (2) regional heterogeneity exists in the spatial effect in the short and long term; and (3) education, digital infrastructure, and traditional finance are important factors influencing this spatial effect of digital inclusive finance on agricultural green development.

Can the Adjustment of China's Grain Purchase and Storage Policy Improve Its Green Productivity?

While the sustainability of grain production has been extensively studied, there have been few studies focusing on the impact of grain policy adjustment on its sustainable production, and the quantitative relationship between these two aspects and the internal mechanism is not completely clear. The main objective of this paper was to explore the impact of grain purchase and storage policy (GPSP) adjustment on its green productivity by expounding the evolution logic and influence mechanism of GPSP. Therefore, taking maize production as an example, this paper constructs the analysis framework of the evolution logic and influence mechanism, and the super-epsilon-based measure model (Super-EBM) is adopted to measure maize green productivity (MGP) in main producing areas from 1997 to 2019, then two groups of difference-in-differences (DID) models are constructed to study the influence of the temporary purchase and storage policy (TPSP) and the producer subsidy policy (PSP) on MGP. The main conclusions include: the implementation of TPSP reduces MGP in Heilongjiang, Jilin, Liaoning and Inner Mongolia (experimental group), whereas the implementation of PSP improves MGP in these provinces is due to the difference in policy effects under the different regulatory objectives and measures; under the demonstration effect of two policies, the increase in effective irrigation and agricultural financial expenditure are important factors to improve MGP, but the backwardness of agricultural mechanization has been hindering the improvement of MGP; after the reform from TPSP to PSP, the continuous increase in production capacity hinders the improvement of MGP under the support effect, the impacts of farmers’ income and agricultural production price on MGP both shift from negative to positive under the wealth effect, and the influence of production agglomeration on MGP shifts from negative to positive under the siphon effect. The excessive implementation of GPSP has seriously affected the sustainability of grain production, thus, this study has certain practical significance and guiding value. The paper emphasizes that the effective way to achieve sustainable food production is to combine the adjustment of GPSP with improving the subsidy mechanism, enhancing the agricultural mechanization and maintaining the appropriate scale of operation.

Regional Differences and Convergence of Inter-Provincial Green Total Factor Productivity in China under Technological Heterogeneity

Green development is an effective way to reconcile the main contradictions between resources, environment, and regional development. Green total factor productivity (GTFP) is an important index to measure green development; an undesirable output-oriented SBM-DEA model and GML model can be used to calculate GTFP. China’s 30 provinces (municipalities and autonomous regions) are divided into three groups: eastern, central, and western. The common frontier function and group frontier function are established, respectively, to deeply explore the temporal and spatial evolution characteristics and center of gravity shift of inter-provincial green total factor productivity (GTFP) in China, and test the convergence under group frontier, to compare the convergence problems under different regions. This study aims to point out the differences in economic growth in different regions of China, foster regional coordination and orderly progress, promote China’s green development process, and improve the high-quality economic development level. According to the results, the efficiency of green development is more reasonable under the frontier groups. The average TGR in the eastern region was 0.993, indicating that it reached 99.3% of the meta-frontier green development efficiency technology. The inter-provincial GTFP in China gradually increased, with an average value of 1.043, which means China’s green development and ecological civilization construction have achieved remarkable results and the three regions showed significant differences. Judging from the shift path of the spatial center of gravity, the spatial distribution pattern of inter-provincial GTFP in China tends to be concentrated and stable as a whole. Moreover, σ convergence only exists in the western region, while absolute β convergence and conditional β convergence exist in eastern, central, and western regions, indicating that the GTFP of different regions will converge to their stable states over time. The results provide a basis for improving the efficiency of institutional allocation of environmental resources, implementing regional differentiated environmental regulation policies, and increasing the value creation of factor resources, which is of great significance for realizing the high-quality economic development in which resources, environment, and economy are coordinated in China.

Spatial-Temporal Evolution and Influencing Factors of Urban Green and Smart Development Level in China: Evidence from 232 Prefecture-Level Cities

Green and smart city is an optimal choice for cities to realize their modernization of governance capacity and sustainable development. As such, it is necessary to clarify the evolutionary characteristics and driving mechanism of urban green and smart development level (GSDL) systematically. From the perspective of green total factor productivity (GTFP), this study adopted the SBM-GML (slack-based model & global Malmquist–Luenberger) method to measure the urban GSDL considering smart input-output elements. Based on the panel data of China’s 232 prefecture-level cities from 2005 to 2018, the spatial and temporal evolution characteristics of urban GSDL were explored, and the factors and structural mutation points affecting urban GSDL were analyzed with quantile regression tests and threshold regression tests. The findings of this paper showed that (1) there is an upward trend in the volatility of urban GSDL from 2005 to 2018, in which the eastern region was highest, followed by the central and western regions, and the differentiation showed no converge among regions; (2) the effect of technical progress and technical efficiency improvement on the urban GSDL was demonstrated with a fluctuating “Two-Wheel-Drive” trend on the whole; (3) the urban GSDL was promoted by the opening-up level and urban scale significantly, while inhibited by the level of economic development and government size. Additionally, the effects of industrial structure, financial development level, and human capital level on the urban GSDL were distinctive at different loci; (4) the threshold effects of economic and financial development level on improving the positive effects of industrial structure and opening-up level on urban GSDL were significant. These findings may enrich the research literature on the evolutionary heterogeneity of green and smart cities and provide theoretical and practical exploration for the construction of green and smart cities.

Environmental Regulation, Rural Residents' Health Investment, and Agricultural Eco-Efficiency: An Empirical Analysis Based on 31 Chinese Provinces

This paper explores the effects of environmental regulation (ER) and rural residents’ health investment (RRHI) on agricultural eco-efficiency (AEE) to provide a reference for the Chinese Government and other developing countries for implementing environmental regulation policies and to provide new paths to further improve green development in agriculture. Using the panel data of 31 Chinese provinces from 2009–2018, the Super-SBM model was used to measure AEE. The role of ER on AEE was analyzed based on panel two-way fixed effects with endogeneity treatment and a robustness test, and this mediating effect analysis was conducted to analyze the role of RRHI in ER and AEE, examining the extent of the effect of ER on AEE in three regions of China—eastern, central and western—using a heterogeneity analysis. The results of the study show that: (1) from a national perspective, ER has a significant positive impact on AEE, showing that ER is effective at this stage; (2) when RRHI is used as a mediating variable, the rising ER’s intensity can promote AEE by increasing RRHI; and (3) the results of the heterogeneity analysis show that ER has the greatest impact on AEE in the economically developed eastern region; the western region with a weaker level of economic development is in second place. However, ER has a negative impact on AEE in the central region with a medium level of economic development. Thus, the impact of ER on AEE will show great differences depending on the stage of economic development.

Spatial-Temporal Evolution of Total Factor Productivity in Logistics Industry of the Yangtze River Economic Belt, China

The logistics industry plays a great role in the sustainable economic development of the Yangtze River Economic Belt (YREB). This paper measures the total factor productivity (TFP) of the logistics industry by using the DEA-Malmquist index method and analyzes its spatial-temporal evolution characteristics based on panel data of 11 provinces and cities in the YREB in 2003–2017. Lastly, a spatial autocorrelation analysis was conducted in conjunction with the exploratory spatial data analysis (ESDA) model. The results show that the overall development of the logistics industry has been relatively good, with an inverted “N” shape trend over the years. Technological progress is the main reason for the growth of TFP. From a regional perspective, it shows a spatial distribution pattern of high in the east and low in the west, with an overall upward trend of TFP levels. The spatial correlation between the TFP levels of logistics in each province and city is gradually increasing, but coordinated development between regions is still limited. Finally, according to the conclusions, policy recommendations are proposed to accelerate the coordinated development of regional logistics and the innovative development of the modern logistics industry.

Digital Economy Development, Industrial Structure Upgrading and Green Total Factor Productivity: Empirical Evidence from China's Cities

The digital economy is an important engine to promote sustainable economic growth. Exploring the mechanism by which the digital economy promotes economic development, industrial upgrading and environmental improvement is an issue worth studying. This paper takes China as an example for study and uses the data of 286 cities from 2011 to 2019. In the empirical analysis, the direction distance function (DDF) and the Global Malmquist-Luenberger (GML) productivity index methods are used to measure the green total factor productivity (GTFP), while Tobit, quantile regression, impulse response function and intermediary effect models are used to study the relationship among digital economy development, industrial structure upgrading and GTFP. The results show that: (1) The digital economy can significantly improve China’s GTFP; however, there are clear regional differences. (2) The higher the GTFP, the greater the promotion effect of the digital economy on the city’s GTFP. (3) From a dynamic long-term perspective, the digital economy has indeed positively promoted China’s GTFP. (4) The upgrading of industrial structures is an intermediary transmission mechanism for the digital economy to promote GTFP. This paper provides a good reference for driving green economic growth and promoting the environment.

Does Agricultural Mechanization Improve the Green Total Factor Productivity of China’s Planting Industry?

Agricultural mechanization is an important factor to improve the green total factor productivity of the planting industry, which is the key way to realize the sustainable development and high-quality development of agriculture. Based on the panel data of 30 provinces in China from 2001 to 2019, this paper uses the stochastic frontier analysis method of the output-oriented distance function to measure the green total factor productivity of China’s planting industry based on net carbon sinks, and empirically studies the impact of agricultural mechanization on the green total factor productivity in China’s planting industry. The main findings of this paper are as follows: (1) Agricultural mechanization can promote the planting green total factor productivity significantly, and this basic conclusion is still robust after using instrumental variables and sub sample regression. (2) The path of agricultural mechanization on planting green total factor productivity is mainly reflected in technology progress and spatial spillover, while the mechanisms of operation scale expansion, factor allocation optimization and technical efficiency change are not significant. (3) With the improvement in the mechanization level, the promotion effect of mechanization on planting GTFP will become clearer. Given these findings, the paper adds considerable value to the empirical literature and provides various policy and practical implications.

A critical assessment of provincial-level variation in agricultural GHG emissions in China

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.

Sustainable Agricultural Total Factor Productivity and Its Spatial Relationship with Urbanization in China

The growth of agricultural total factor productivity (TFP) is seen as a driving force for the sustainable development of agriculture. Meanwhile, the promotion of urbanization in China has exerted a profound impact on agricultural production. This paper calculates the agricultural TFP and analyzes the effect of urbanization. Firstly, the DEA-Malmquist method is used to calculate the dynamic change in agricultural TFP in China from 2004 to 2016. Secondly, the spatial spillover effect of urbanization on agricultural TFP is investigated by the spatial Durbin model. We found that: the average annual growth rate of agricultural TFP in China is 4.8% from 2004 to 2016; and the spillover effect of urbanization on agricultural TFP shows a U-shaped relationship, which means that urbanization has exerted a negative effect first and then a positive effect on agricultural TFP. Finally, the paper puts forward policy suggestions from the perspective of sustainable coordination of urbanization and agricultural production.

Difference in carbon footprint between single- and double-cropping rice production in China, 2003-2016

Agricultural greenhouse gas (GHG) emissions account for 14% of the total greenhouse gas (GHG) emissions from human activities, and the carbon footprint (CF) of agricultural production, which can help to propose positive measures to mitigate greenhouse gas (GHG) emissions, is a general method for assessing the impact of agricultural practices on the external environment. This article calculated the carbon footprint (CF) of rice production and compared the differences between the double-and single-cropping rice regions, which is rarely mentioned in previous literature. Some interesting information was shown. For example, the internal structure of rice production carbon footprint (CF) is prominent. (a) In terms of time evolution, CF of agricultural materials showed an increasing trend year by year, while CF of rice planting remained basically stable. (b) In terms of regional differences, whether single-cropping rice regions or double-cropping rice regions, CF of agricultural materials did not show the previous increasing trend after 2011, especially after 2015. This may be greatly affected by the policy such as the abolishing of the China agricultural tax in 2006. These studies can help us to reveal how agricultural policies and different rice cropping patterns affect each region.

Analyzing the spatial network structure of agricultural greenhouse gases in China

Investigating the regional correlation and factors affecting agricultural greenhouse gas (GHG) emissions can help establish a regional mechanism for the synergistic reduction of emissions and produce chain-like reductions. Different from the traditional geographical relationship analysis framework, linear analysis ideas, we use social network analysis to discern the regional correlations in agricultural GHG emissions from a relational network viewpoint, clarify the network functions of each node, and explain agricultural GHG correlation from a spatial, economic, and technological viewpoint by nonparametric regression. The results indicate that (1) the emission network is stable and there is a relationship of control between regions, (2) Central China is the most important region in agricultural GHG networks; however, the importance of the northwest and southwest has increased; the northeast has remained relatively independent, (3) influencers are mainly concentrated in the middle of the Yangtze River and the northwest, while dependentors are concentrated in municipalities such as Beijing and Tianjin, and the coastal regions in the southeast, and (4) the interprovincial agricultural GHG correlation can be enhanced by shortening the spatial distance, strengthening economic ties, and increasing the diffusion of technology. Implementing a "leader-follower" strategy according to the role of each region and enhancing the intermediator's "conduit" role will ultimately lead to the formation of an interprovincial interactive and cooperative emission reduction mechanism.

The effect of trade openness on the relationship between agricultural technology inputs and carbon emissions: evidence from a panel threshold model

The development of low-carbon agriculture systems has been a global consensus to reduce carbon emissions in the agricultural sector for addressing climate change challenges. This fact brings the need to study the agricultural carbon emissions (ACEs). Studies focusing on calculating the spatiotemporal changes of ACEs and analyzing the main factors for ACE changes have been conducted. The agricultural technology inputs (ATIs) as an important factor to influence ACEs have been identified. The traditional linear model was the commonly used method to study the relationship between ATIs and ACEs, whereas the impact of ATIs on ACEs in different areas might be complex and nonlinear due to the differences in trade openness causing different development levels of agricultural technologies. Therefore, this study aims to investigate the effect of trade openness on the relationship between ATIs and ACEs using a panel threshold model and put forward policy implications for the low-carbon agriculture development. The analysis was based on data from a panel of 31 provinces of China during 2003-2018. The results show that ATIs and ACEs increased from 2003 to 2018 and the spatial distribution of ATIs was similar to that of ACEs. The ATIs had a positive effect on ACEs with a significant single-threshold effect from trade openness. When the trade openness was below the threshold (0.1425), the positive effect of ATIs on ACEs was significant (coefficient, 0.117), whereas, when the trade openness was above the threshold (0.1425), the positive effect of ATIs on ACEs significantly decreased (coefficient, 0.062). Furthermore, industrial structure and agricultural economic development were the positive drivers of ACEs, while trade openness, education level of rural workers, R&D funding, and natural disasters had negative relationships with ACEs. The results provide valuable references for understanding ACE drivers and developing low-carbon agriculture with the consideration of ATIs and trade openness.

The Impact of Planting Industry Structural Changes on Carbon Emissions in the Three Northeast Provinces of China

This paper focuses on the impact of changes in planting industry structure on carbon emissions. Based on the statistical data of the planting industry in three provinces in Northeast China from 1999 to 2018, the study calculated the carbon emissions, carbon absorptions and net carbon sinks of the planting industry by using crop parameter estimation and carbon emissions inventory estimation methods. In addition, the multiple linear regression model and panel data model were used to analyze and test the carbon emissions and net carbon sinks of the planting industry. The results show that: (1). The increase of the planting area of rice, corn, and peanuts in the three northeastern provinces of China will promote carbon emissions, while the increase of the planting area of wheat, sorghum, soybeans, and vegetables will reduce carbon emissions; (2). Fertilizer application, technological progress, and planting structure factors have a significant positive effect on net carbon sinks, among which the changes in the planting industry structure have the greatest impact on net carbon sinks. Based on the comprehensive analysis, it is suggested that, under the guidance of the government, resource endowment and location advantages should be given full play to, and the internal planting structure of crops should be reasonably adjusted so as to promote the development of low-carbon agriculture and accelerate the development process of agricultural modernization.

Review of evolution of the public long-term care insurance (LTCI) system in different countries: influence and challenge

Background

The growing demand for LTC (Long-term care) services for disabled elderly has become a daunting task for countries worldwide, especially China, where population aging is particularly severe. According to CSY (China Statistical Yearbook,2019), the elderly aged 65 or above has reached 167 million in 2018, and the number of disabled elderly is as high as 54%. Germany and other countries have alleviated the crisis by promoting the public LTCI (Long-Term Care Insurance) system since the 1990s, while China’s public LTCI system formal pilot only started in 2016. Therefore, the development of the public LTCI system has gradually become a hot topic for scholars in various countries, including China.

Methods

This review has been systematically sorted the existing related literature to discuss the development of public LTCI (Long-Term Care Insurance)system form four aspects, namely, the comparison of public LTCI systems in different countries, the influence of public LTCI, challenge of public LTCI, and the relationship between public LTCI and private LTCI. We searched some databases including Web of Science Core Collection, Medline, SCOPUS, EBSCO, EMBASE, ProQuest and PubMed from January 2008 to September 2020. The quality of 38 quantitative and 21 qualitative articles was evaluated using the CASP(Critical Appraisal Skills Programme) critical evaluation checklist.

Results

The review systematically examines the development of public LTCI system from four aspects, namely, the comparison of public LTCI systems in different countries, the influence of public LTCI, the challenge of public LTCI, and the relationship between public LTCI and private LTCI. For example, LTCI has a positive effect on the health and life quality of the disabled elderly. However, the role of LTCI in alleviating the financial burden on families with the disabled elderly may be limited.

Conclusion

Some policy implications on the future development of China’s LTCI system can be obtained. For example, the government should fully consider the constraints such as price rise, the elderly disability rate, and the substantial economic burden. It also can strengthen the effective combination of public LTCI and private LTCI. It does not only help to expand the space for its theoretical research but also to learn the experiences in the practice of the LTCI system in various countries around the world. It will significantly help the smooth development and further promote the in-depth reform of the LTCI system in China.

Overcoming Barriers to Agriculture Green Technology Diffusion through Stakeholders in China: A Social Network Analysis

It is crucial to actively encourage the development of agriculture green technology, which has been regarded as one of the most effective solutions to the environmental degradation caused by agricultural activities. However, agriculture green technology diffusion is indeed a challenging task and still faces numerous barriers. The stakeholders who can potentially deal with these barriers, however, have been overlooked by previous studies. To address these issues, social network analysis was performed to identify critical stakeholders and barriers. Their interactions in agriculture green technology diffusion were analyzed based on the literature, a questionnaire survey and expert judgments. A two-mode network and two one-mode networks were used to analyze the relationships among the identified 12 barriers and 14 stakeholders who can influence these 12 barriers identified. The results show that agricultural research institutes, universities, agribusiness, agencies of township promotion, the government and farmers’ relatives are key stakeholders and that the limited market demand for green technology and the high cost of its diffusion are two main barriers. However, poor green technology operability and farmer families in distress are factors that are not as important as previously perceived. Finally, some recommendations and suggestions are provided to promote agriculture green technology diffusion in China.

Effect Evaluation of the Long-Term Care Insurance (LTCI) System on the Health Care of the Elderly: A Review

Background

How to cope with the rapid growth of LTC (long-term care) needs for the old people without activities of daily living (ADL), which is also a serious hazard caused by public health emergencies such as COVID-2019 and SARS (2003), has become an urgent task in China, Germany, Japan, and other aging countries. As a response, the LTCI (long-term care insurance) system has been executed among European countries and piloted in 15 cities of China in 2016. Subsequently, the influence and dilemma of LTCI system have become a hot academic topic in the past 20 years.

Methods

The review was carried out to reveal the effects of the LTCI system on different economic entities by reviewing relevant literature published from January 2008 to September 2019. The quality of 25 quantitative and 24 qualitative articles was evaluated using the JBI and CASP critical evaluation checklist, respectively.

Results

The review systematically examines the effects of the LTCI system on different microeconomic entities such as caretakers or their families and macroeconomic entities such as government spending. The results show that the LTCI system has a great impact on social welfare. For example, LTCI has a positive effect on the health and life quality of the disabled elderly. However, the role of LTCI in alleviating the financial burden on families with the disabled elderly may be limited.

Conclusion

Implementation of LTCI system not only in reducing the physical and mental health problems of health care recipients and providers, and the economic burden of their families, but also promote the development of health care service industry and further improvement of the health care system. However, the dilemma and sustainable development of the LTCI system is the government needs to focus on in the future due to the sustainability of its funding sources.

Exploring environmental efficiency and total factor productivity of cultivated land use in China

During cultivated land use, nonseparable relationships exist between certain inputs and outputs. This study explored the influence of the nonseparable characteristics of input and output variables on both the cultivated land use environmental efficiency (CLUEE) and the cultivated land use environmental total factor productivity (CLUETFP). To evaluate China's CLUEE and CLUETFP from 1997 to 2017, we used the nonseparable hybrid model with undesirable outputs (NSH-U) and the nonseparable hybrid Malmquist (NSH-M) productivity index. The results showed the following: (1) The CLUEE differed significantly among regions, with the CLUEE decreasing from the eastern region to the western, northeastern, and central regions. We observed large differences in the CLUEE among provinces. (2) Nonradial input inefficiency and radial output inefficiency were the primary sources of cultivated land use environmental inefficiency. (3) Technical progress was the primary driving force behind the growth of CLUETFP across the entire country and in the eastern, central, western, and northeastern regions; however, technical efficiency limited the growth of CLUETFP to a certain extent. Finally, we proposed policy implications to improve China's CLUEE and CLUETFP.

How Much Is the Eco-Efficiency of Agricultural Production in West China? Evidence from the Village Level Data

This study evaluates the eco-efficiency of agriculture in Pupiao Town, in the Yunnan province of China, through micro-level research. The term "eco-efficiency" refers to the efficiency with which ecological resources are used to meet human needs. Interviews and field research were conducted to collect the data of the 23 villages from 2016 to 2018. The Data Envelopment Analysis model (DEA) was used for data analysis. The results were as follows: (1) The eco-efficiency scores of Pupiao Town had considerable spatial heterogeneity, exhibiting a general trend of higher in the middle and lower in the east and west, which suggested eco-efficiency may be correlated with topography and transportation. (2) The value of eco-efficiency for the entire town had considerable areas for improvement and showed a slow-growth trend. (3) Fertilizers, pesticides, agricultural diesel, agricultural carbon emission, and non-point source pollution had a significant impact on eco-efficiency, followed by agricultural labor and arable land. (4) Agricultural chemicals were primary determinants affecting eco-efficiency. Most of the factors had a stronger effect on the eastern and western regions. The study suggests that transportation should be improved to promote the conveyance of market information and the application of more efficient and productive farm methods. The most important way is to improve effective utilization and to reduce the amount of agricultural chemicals. In addition, it is necessary to offer technical training and help to support farmers in upgrading their farm operations.

Does COVID-2019 have an Impact on the Purchase Intention of Commercial Long-Term Care Insurance among the Elderly in China?

PURPOSE

As an important measure to alleviate long-term care (LTC) costs for the disabled due to the aging of the population, long-term care insurance (LTCI) system has been paid more attention in China. In addition to the government-led public LTCI system that has been piloted in cities such as Qingdao, Chongqing and Shanghai, health insurers such as the China Life Insurance Company are also experimenting with various types of commercial LTCI in the private market. However, the commercial LTCI market is developing very slowly due to public awareness and other reasons. On the other hand, COVID-2019 has had an impact on the cognition of the importance of long-term care for the elderly due to the fact that the death cases of COVID-2019 have been mainly concentrated in the elderly population with chronic diseases such as hypertension. Therefore, the purpose of this study is to explore the differences in the purchase intention of commercial LTCI among the elderly in two different periods: before and after the outbreak of COVID-2019.

METHODS

By using the Andersen behavioral model and two investigations in two different periods before and after the outbreak of COVID-2019, this study explores the impacts of COVID-2019 on the purchase intention of commercial LTCI.

RESULTS

Some significant discoveries were found. For example, 25.8% of interviewees showed purchase intention in LTCI in the time before the COVID-2019 outbreak, while this proportion increased to 37.6% after the COVID-2019 outbreak. People who were younger (OR = 2.128, before COVID-2019; OR = 1.875, after COVID-2019) or who had more education (OR = 1.502, before COVID-2019; OR = 2.218, after COVID-2019) were more interested in commercial LTCI.

CONCLUSION

This study shows that COVID-2019 has had an obvious impact on the purchase intention of commercial LTCI, which provides some enlightenment for China to improve the LTCI system in the future, especially to accelerate the development of commercial LTCI. For example, it is essential to promote the importance of long-term care among the elderly in a focused and targeted way. In terms of the key target audience, it can be developed gradually from the groups with higher education levels and the middle elderly aged 45-64 years old.

Performance Evaluation of Enhanced Bioretention Systems in Removing Dissolved Nutrients in Stormwater Runoff

Bioretention has great potential in managing and purifying urban stormwater runoff. However, information regarding the removal of nutrients in bioretention systems with the use of media, plants, and saturated areas is still limited. In this study, three devices of control, conventional bioretention (DS), and strengthened bioretention (SZ) were investigated to enhance the simultaneous removal of nitrogen and phosphorus. The experimental column SZ showed the best performance for total phosphorus (TP), ammonia (NH4+-N) and total nitrogen (TN) removal (85.6–92.4%, 83.1–92.7%, 57.1–74.1%, respectively), whereas DS columns performed poorly for NH4+-N removal (43.6–81.2%) under different conditions. For the removal of nitrate, the columns of Control and DS exhibited negative performance (−14.3% and −8.2%) in a typical event. Further evaluation of water quality revealed that in the early stages of rainfall, the effluent of the SZ column was able to reach quality standards of Grade IV for surface water in China. Moreover, although the ion-exchange and phosphate precipitation occurred on the surface of the media, which were placed in the saturation zone, it did not change the surface crystal structure.

Comparison of the Measurement of Long-Term Care Costs between China and Other Countries: A Systematic Review of the Last Decade

BACKGROUND

The rapid aging of populations in some countries has led to a growing number of the disabled elderly, creating a huge need for Long-Term Care (LTC) and meeting its costs, which is a heavy economic burden on the families of the disabled elderly and governments. Therefore, the measurement of Long-Term Care (LTC) costs has become an important basis for the government to formulate Long-Term Care (LTC) policies, and academic research on Long-Term Care (LTC) costs is also in the process of continuous development and deepening.

METHODS

This is a systematic review that aims to examine the evidence published in the last decade (2010-2019) regarding the comparison of the measurement of Long-Term Care (LTC) costs between China and other countries.

RESULTS

Eighteen Chinese studies and 17 other countries' studies were included in this review. Most Chinese scholars estimated long-term care costs based on the degree of disability among the disabled elderly. However, the studies of European and American countries are more and more in-depth and comprehensive, and more detailed regarding the post-care cost of specific diseases, such as Parkinson's disease, Alzheimer's disease, and epilepsy.

CONCLUSION

In future academic research, we should fully consider the human value of long-term care providers and further study the differences in the long-term care costs of different chronic diseases. In China's future policymaking, according to the experience of Germany, Sweden, and other countries, it may be an effective way to develop private long-term care insurance and realize the effective complementarity between private long-term care insurance and public long-term care insurance (LTCI).

Application of Threshold Regression Analysis to Study the Impact of Clean Energy Development on China's Carbon Productivity

Facing the pressures of international carbon emission reduction, the transformation into a low-carbon economy has become a common issue of all countries. The core of developing a low-carbon economy is to increase carbon productivity, which can be measured as the economic benefits of unit carbon emissions. Therefore, using province-level panel data in China from 2009 to 2017, we analyze the carbon productivity level of each region, and empirically investigate the threshold effect of clean energy development on carbon productivity under different technological innovation levels. The results show that the carbon productivity is rising, and China’s economic development pattern has been shifting towards low-carbon and sustainable development. Furthermore, the driving force of clean energy development on carbon productivity is not monotonously increasing (decreasing) but is a “double threshold effect” of technological innovation capability. Finally, based on the research conclusions and realistic requirements of China’s low-carbon economic transformation, this paper proposes improving carbon productivity from the aspects of innovation capability improvement and institutional guarantee.