Climate change disrupts the balance of natural ecosystems and threatens the sustainable development of human society. As the leading industry in many countries, manufacturing promotes economic ...growth; unfortunately, it also emits large quantities of greenhouse gases. Thus, it is necessary to transform the production pattern of manufacturing into green production. Although technology innovation is the only way to tackle the issue, different types of technology innovation may lead to various environmental performances. We argue that low-carbon technology innovation (LCTI) is the key to green production. Using data of Economic Co-operation and Development (OECD) countries from 1990 to 2014, we use the patent-stock method to measure LCTI levels and analyze its development trend in OECD countries. Based on the shepherd distance function, we measure carbon efficiency and carbon productivity by the fixed-effect Stochastic Frontier Analysis (SFA) model. Then we investigate the effect of LCTI on carbon emission efficiency in manufacturing by the fixed-effect regression model. After controlling some variables, evidence shows a significant positive influence of LCTI on the environmental performance of manufacturing. The level of LCTI constantly increased in OECD countries during the study period. Among these countries, the level of low-carbon technology in the chemical industry is the highest; in most countries, the low-carbon technology of the production process grows fastest. Policy implications are further discussed.
The progress of agricultural green technology is an important means and fundamental way to achieve high-quality development of agriculture. The current study takes the panel data of 31 provinces in ...China from 1998 to 2018 and uses the Epsilon Based Measure-Global Malmquist-Luenberger (EBM-GML) model to measure China’s agricultural green technological progress (AGTP) and discusses its dynamic evolution characteristics in the spatiotemporal dimensions. Finally, we analyze the spatial spillover effects of AGTP by the spatial Dubin model. The results show that China’s AGTP showed a trend of first rising and then falling, and the average value is 1.0525. AGTP has obvious regional unbalanced development, and the regional differences are expanding. It shows that AGTP between adjacent areas is closely linked. The Moran’s
I
index shows that AGTP has a significant positive spatial correlation. The local Moran’s
I
index shows that AGTP is concentrated in Northwest, Northeast, and North China, and green technological is degraded in East and South China. From the spatial spillover effects of AGTP, the level of agricultural economic development, real GDP per capita, and urbanization have significantly promoted AGTP in the local and neighboring areas, while the agricultural internal structure and the level of labor inhibit AGTP in the local and neighboring areas. In addition, the administrative environmental policy (ENVP) and the economic environmental policy (ECOP) have negative impacts in neighboring areas, while the policy has negative spillover effects and positive spillover effects in the local area, respectively. Therefore, we should adhere to the concept of green development, pay attention to the regional exchange of green technology, concentrate policies on low-low concentration areas, and increase the follow-up tracking and supervision mechanism of the policy design and implementation process.
China is facing increasingly severe challenges in its quest to achieve urbanization and mitigate CO2 emissions. The existing studies have usually introduced a single indicator to describe ...urbanization and have ignored the complexity and multi-dimensionality of urbanization. This study establishes an evaluation system of urbanization quality to estimate the urbanization development level. The geographically weighted regression (GWR) model is employed to examine the impact of the urbanization quality on CO2 emissions and reveals the spatial differences of 30 provinces in 2000, 2005, 2010, and 2015. The results show that there are significant temporal and spatial differences in the effects of the urbanization quality on CO2 emissions between provinces. Improvements in the urbanization quality have contributed to cutting CO2 emissions in most provinces. The impact of the urbanization quality on CO2 emissions in the central region and western region is greater than that in the eastern region. The energy intensity has the largest positive impact on CO2 emissions, which indicates that technical progress can effectively reduce CO2 emissions. The industrial structure has a positive impact on CO2 emissions in 2000 and 2015, whereas it has a negative impact on the CO2 emissions of some provinces in 2005 and 2010. This paper provides valuable findings and conclusions of the relationship between urbanization quality and CO2 emissions. Differentiated policy implications are proposed according to geographical differences.
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•This study establishes an evaluation system of urbanization quality (UQ).•This study examines the spatial effect of urbanization quality on CO2 emissions.•There are obvious differences in the effect of UQ on emissions between provinces.•Energy intensity has the largest positive impact on CO2 emissions.•Industrial structure has a positive impact on CO2 emissions in 2000 and 2015.
•China has been retiring degraded cropland and expanding forest covers.•Changes in land use have accelerated labor transfer into off-farm sectors.•Farming has been intensified to offset the effects ...of cropland reduction.•Average household income increased by 250%, leading to large reduced poverty.
As the largest payment for ecosystem services initiative in the developing world, China's Sloping Land Conversion Program subsidizes households to restore marginal croplands and other degraded fields. While it has attracted broad attention, many questions regarding its performance remain unanswered. Using descriptive and econometric analyses based on a longitudinal dataset containing a large number of surveyed households over 1999–2008, we examine the multi-faceted changes in program enrollment, land and labor allocation, agricultural production, and income structure and inequality. We find that the program has affected land use substantially by simultaneously retiring degraded cropland and increasing forest and vegetation covers, which have accelerated labor transfer into off-farm sectors. Meanwhile, households have intensified agriculture by increasing their production expenditures, enabling them to offset some of the negative effects of the cropland set-aside and reduced farm labor use. While the subsidies have been a significant source of income to the participants, most households have had a larger portion of their income come from non-farming jobs, leading to the increase of average family income by over 250%, and the reduction of rural poverty and thus the most vulnerable population. As impressive as these changes may be, the program still faces great challenges before the ecosystems are adequately recovered to provide their services.
An objective understanding of the current situation and influencing factors of rural green development in China is an important prerequisite for effective formulation making of green development ...policies. Based on the panel data of 31 provinces of China from the year 1997 to 2017, this paper constructs and measures the rural green development efficiency (RGDE) based on Driving-Force, Pressure, State, Influence, Response (DPSIR) model and super-efficiency slacks-based measure (SBM) model. The results show that, the overall RGDE in China is fluctuating and rising from 1997 to 2017, and there are some differences between and within regions. The RGDE in developed areas is higher than that in developing areas, and coastal areas are higher than that in inland areas. The entire country, eastern, central, and western regions show
σ
convergence, which indicates that the RGDE is getting better, but there is no absolute
β
convergence, that means there is no “catch-up effect” between regions, but the gradient divergence showed central > eastern > western. At the same time, there is no conditional
β
convergence; the initial RGDE has a positive impact on the growth rate of RGDE, and the financial self-sufficiency rate promotes the growth of RGDE of the whole country and the western region, but inhibits the improvement of RGDE of the eastern region. The per capita GDP, mechanization degree, and agricultural industrial structure in rural areas did not promote the growth of RGDE. Based on the results, this paper puts forward some policy suggestions, such as promoting the classified implementation of rural green development policies, strengthening the top-level design, optimizing the existing agricultural mechanism and system, and guiding and standardizing the farmers’ green production behavior.
Economies that depend on natural resources can experience a resource drag effect when economic growth is limited by constraints on the availability of those resources. Therefore, this study uses ...panel data and the improved Solow growth model to explore the resource drag effect on China’s regional economic growth from 1987 to 2017 and makes innovative contributions to address these four gaps in the previous literature: the resources gap, the consistent measurement gap, the regional gap, and the temporal gap. The empirical results indicate that the resource drag effect reduced China’s overall annual economic growth by 0.58% during the study period, with reductions of 1.07%, 0.29%, 0.79%, and 0.46% in the Eastern, Western, Central, and Northeastern regions, respectively. In the meantime, the resources drag effect changed in individual regions and across regions. The results on energy drag are most notable. Policies such as “West-to-East Electricity Transmission” and “West-to-East Gas Transmission” promoted economic growth of the Eastern and Western Region, facilitating continued growth in both regions and attracted the return of labor to the Western region. The results indicate that the policies such as west-to-east energy transfer for helping to even out the economic growth conditions in different regions. Labor force mobility has also been important to alleviate resource dependence of agricultural production in Central regain, while other regions have managed to continually grow through improvements in inefficiency. Also, growth in some regions/provinces continues to depend upon increases in water, land, and energy availability and export. This will become increasingly problematic as the social prices of these inputs rise to account for environmental damage. Therefore, the government should adjust the industrial structure of each region to optimize use of resource endowments, alleviate dependence on natural resources, and achieve sustainable economic development.
Improving the ecological efficiency of farmland use (EEFU) has become an important part of ensuring food security and solving environmental pollution problems. At present, the Chinese government is ...actively promoting large-scale farmland transfer to reduce the level of farmer-/plot-scale farmland fragmentation (FF), so it is crucial to clarify the effect of landscape-scale FF on EEFU. This study applies the non-dynamic panel and threshold models in an empirical study of the municipal administrative regions along the Yangtze River Economic Belt (2000, 2005, 2010, and 2015). The results reveal that there is a single threshold for the effects of area, shape, and distance fragmentation on EEFU with farmland area per capita (FAPC) as the threshold variable. The threshold values are 1.548, 1.373, and 1.542, respectively. The effects of area and distance fragmentation on EEFU are initially promoted and then suppressed; however, shape fragmentation always has an inhibitory effect on EEFU. These findings suggest that ignoring the condition of FAPC of different regions and promoting large-scale farmland transfer blindly will give rise to the decline of EFFU. These results are conducive to the sustainable utilization of farmland and the formulation of related policies.
China’s economy has a typical characteristic that the government has a strong control of enormous productive resources, such as the land resources. Industrial land use is greatly influenced by ...institutional factors that refer to the market means and government control. But little empirical researches have focused on the performances of government incentives and land market in industrial land use efficiency (ILUE). Based on the geographically and temporally weighted regression model, this study reveals the spatial and temporal role of government incentives and land market on ILUE in China from 2007 to 2015. The results show that the ILUE has spatial and temporal differences in different cities, showing a significant increasing trend from 2007 to 2015. The land transfer fund, land added tax, and official promotion incentive have a negative impact on the ILUE of most cities in 2015. There are significant temporal and spatial variations in the effect of land marketization, land price, the proportion of stock land and the proportion of unused land on ILUE between cities. Finally, this paper provides some policy implications according to the results obtained.
Ecological restoration programs and payments for ecosystem services have both attracted broad academic and policy attention. While they are inherently linked and thus should be part of the integrated ...processes of ecosystem management, they have been largely pursued separately. The majority of restoration ecologists and socioeconomic scholars tend to dwell in their own “comfort zone” and concentrate on different, disciplinary facets of the same issues. However, this situation is not conducive to the accomplishment of their common cause. The objective of this paper is to make a case for more effective efforts in integrating ecological restoration programs and payments for ecosystem services and thus more substantive interdisciplinary collaboration in the science and practice of ecological restoration and ecosystem service provision. To that end, the relevant research developments and bodies of literature are carefully reviewed, and China's recent experience and lessons in retiring and converting degraded cropland extensively presented. It is hoped that these efforts will highlight the challenges and opportunities in the current state of affairs and convince scientists in different disciplines to work together in better and more broadly integrated research of ecological restoration programs and payments for ecosystem services.
► We argue that ecological restoration programs and payments for ecosystem services should feature linked social-ecological systems. ► A survey of the literature and an illustration with China’s experience in converting degraded cropland are used to highlight our view. ► We call for ecologists, economists, and other scholars to work together in truly integrated assessment. ► It is essential to have more interdisciplinary collaboration in the science and practice of ecosystem restoration and management.
Social capital is an integral part of farmers' life, which can be exogenously affected by land recuperation. Based on 1240 farmer field survey data in Gansu Province, this paper used the Logit model ...to analyse the influencing factors of farmers' participation in land recuperation, and used the entropy method to measure social capital from the three dimensions of social network, social trust and social norms, and further used the propensity matching score method to measure the effect of land recuperation on farmers, and then compared the effects under different fixed ages and education groups. The following factors significantly affected farmers' participation in land recuperation: age, years of education, migrant workers' relationships with family and friends, relationship between migrant workers and friends and colleagues in the workplace, number of migrant workers away from home, cultivated land area, and family living standards. Land recuperation had the greatest promotion effect on farmer' social network (163.9%), followed by social trust (28.0%) and social norm (11.3%). According to the results of group differences, land recuperation most significantly affected the social capital of farmers aged 45-55 years and household heads educated for 9-12 years compared to other age and education groups.
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