Since the ecological protection and high-quality development of the Yellow River Basin (YRB) in China have become a primary national strategy, the low-carbon economy is crucial. To formulate effective emission mitigation policies for the YRB, we need to comprehensively understand the characteristics of the spatial agglomeration of the carbon emissions intensity in the YRB and its regional heterogeneity. Therefore, based on the relevant data from 2005 to 2017, we first scientifically measure the carbon emissions intensity of 57 cities along the YRB. Then, we analyze the spatial agglomeration characteristics and long-term transfer trends of carbon emission intensity using exploratory spatial data analysis methods and Markov chains. Finally, the Dagum Gini coefficient and the variation coefficient method are used to study the regional differences and differential evolution convergence of the carbon emissions intensity in the YRB. The results show that the carbon emissions intensity of the YRB has dropped significantly with the spatial distribution characteristics “high in the west and low in the east”, and there is a significant spatial autocorrelation phenomenon. In addition, the probability of a shift in urban carbon intensity is low, leading to a “club convergence” and a “Matthew effect” in general and across regions. Inter-regional differences have always been the primary source of spatial differences in carbon emissions intensity in the YRB, and the intra-regional differences in carbon emissions intensity in the lower YRB show a significant convergence phenomenon. The research results may provide a reference for the regional coordinated development of a low-carbon economy in the YRB, and serve to guide the win-win development model of ecological environment protection and economic growth in the YRB.
The Paris Agreement marks global response to climate change after 2020 and China has proposed the dual carbon goals, carbon peaking and carbon neutrality, in response. This paper analyses the contribution to dual carbon goals by analyzing the impact of environmental regulations (ERs) on green technology innovation (GTI) in China. First, considering variances in energy consumption structure across provinces and industries, industrial CO2 emission is calculated and set as an undesirable output of industrial GTI. Then, industrial green technology innovation efficiencies (GTIE) of 29 provinces in China between 2005–2017 are calculated using a non-oriented two-stage network SBM-DEA model assuming variable returns to scale. Last, dynamic evolution and regional differences of industrial GTIE during green technology R&D, green technology commercialization, and overall GTI stages are respectively observed, and the influences from different types of ERs, command-based (CER), market-based (MER), and voluntary (VER), on industrial GTIE are analyzed. We identify China is overall experiencing relatively low but gradually increasing industrial GTIE and Industrial GTIE present gradient changes across provinces with increasingly prominent regional difference. It is found that influences of types of ERs on industrial GTIE present dynamic effect, threshold effect, lag effect and regional differences.
Abstract-Food and agricultural production in Thailand play an indispensable role in economic growth. The frozen vegetable industry is considered to be important due to its increasing annual exports. The goal of this paper is to measure the efficiency of export frozen vegetables using network DEA. We use the two-stage model to measures efficiency by separating the supply chain structure into suppliers and manufacturers. Index Terms-Network DEA, efficiency measurement, export frozen vegetable, supply chain.
In the past, research on evaluating the portfolio efficiency of mutual funds was mostly based on the mean-variance framework proposed by Markowitz. However, for Chinese short-term mutual funds (CSTMF), short-term mutual funds with different lock-up periods will automatically roll over after the lock up period to restart the funds unless instructed to terminate. By reducing the liquidity of their investments, the lock-up period generates an opportunity cost for investors. The liquidity impacts of different CSTMFs will depend on their lock-up periods. In this paper, we evaluate the portfolio efficiency of the funds by justifying using the lock-up period as a measurement to measure the liquidity risk faced by the investors from the perspective of investors. Therefore, here, we propose to use the return-variance-liquidity framework to evaluate the performance of the funds. Furthermore, we select 28 CSTMF funds to conduct empirical analysis with appropriate DEA models. Firstly, we carry out some comparisons with the traditional mean-variance framework. The results show that the length of the lock-up periods has a significant impact on the fund’s performance, which has important guidance for the investment strategy. Finally, we conduct the benchmarking analysis for the 28 funds and introduce a new DEA model, present how to reduce liquidity risk under the premise of given return and variance, and provide valuable investment advices by combining these with sensitivity analysis. Overall, there are three conclusions: (1) most inefficient funds can be improved using portfolios consisting of funds with shorter lock-up period. (2) For some inefficient funds with shorter lock-up period, the current return and variance levels can be achieved by a portfolio. (3) Among the funds with higher efficiency scores, funds with shorter lock-up period have higher stability.
Green development is crucial to global natural resource conservation, environmental improvement and sustainable development. Furthermore, resource-based cities’ green development is more challenging compared with that of other types of cities. On such basis, it is a necessity to understand the green development level of such cities. Therefore, we introduce green development efficiency (GDE), which is a key indicator for measuring green development. This paper takes China’s 112 resource-based cities during 2010–2019 as its research object, and examines their GDE using the Super-SBM-Undesirable model. Moreover, industrial structure upgrading (ISU) and human capital structure upgrading (HCSU) have important implications for green development. To further explore the influence of ISU and HCSU on GDE, this paper employs a fixed effect model, an interaction effect model and a threshold model. Finally, considering the differences between different resource-based cities, the heterogeneity of ISU and HSCU on GDE in four types of China’s resource-based cities is also explored. It is found that (1) although GDE is on the track of steady improvement, the overall GDE was still relatively low during 2010–2019, with an average GDE of about 0.8; (2) ISU, HCSU and their interaction can promote GDE in resource-based cities and with the intensity of industrial structure increasing, the interaction effect of ISU and HSCU on GDE in resource-based cities shifts from positive to negative; (3) there exists heterogeneity in the direct effect and interaction effect of ISU and HCSU among four types of resource-based cities (i.e., mature cities, growing cities, declining cities and regenerating cities). Our findings offer a data reference for the green and sustainable development of China’s resource-based cities, and also a method reference for other countries’ resource-based cities.
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