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

Huang, Xiuquan; Feng, Chao; Qin, Jiahong; Wang, Xi; Zhang, Tao

doi:10.1016/j.scitotenv.2022.154477

Cited by 120 publications

(72 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results of the TFP growth estimates of the marine economy using green indicators are significantly lower than those using traditional indicators. This could be because traditional evaluation indicators overlook the negative environmental consequences of marine economic development, allowing the TFP growth of the marine economy to be overestimated (Huang et al, 2022).…”

Section: Meta-regression Results At the Provincial Levelmentioning

confidence: 99%

Total factor productivity of China’s marine economy: A meta-analysis

Dong

Qiao²,

Yuan³

et al. 2022

Front. Mar. Sci.

View full text Add to dashboard Cite

With the rapid development of the global economy and the depletion of land resources, the ocean has gradually become a new area for human society to seek resources and space utilization. In China, the marine economy also has become an essential part of the national economy. Therefore, it is crucial to evaluate marine productivity to understand China's marine industry's inputoutput status and development level. We conducted a meta-analysis using 622 observations from 33 primary empirical studies to quantify the discrepancies in measurement findings and identify the influencing factors of total factor productivity (TFP) growth in the marine industry. The results indicate that: (1) In the existing literature, the mean TFP growth of China's marine economy at the provincial level is 1.002, which is lower than that at the national level (1.022);(2) The time span of the data, the estimation model, the evaluation indicator, and the journal's rank all have a significant effect on the estimation results of the marine economic TFP growth, whereas the year of publication does not affect the estimation results; (3) The TFP growth of China's marine economy peaked during the Ninth Five-Year Plan period, after which it began to decline year after year; (4) The TFP growth of the marine economy in the Yangtze River Delta region and Circum Bohai Sea region is significantly higher than that in the Pan-Pearl River Delta region; (5) Economic factors such as gross ocean product, level of opening-up, level of marine science and technology, and industrial structure all have an impact on the marine economic TFP growth. Accordingly, the following insights were obtained: In terms of marine economic development policies, we should continue to enhance the investment in marine environmental governance, strengthen the construction of marine ecological civilization, and pay attention to synergistic regional development, opening up to the outside world, scientific and technological innovation, and industrial structure optimization. In addition, the follow-up study should use long-period sample data as much as possible, pay attention to the parametric SFA model, and strip the negative environmental impact by constructing a green evaluation index system.

show abstract

Section: Meta-regression Results At the Provincial Levelmentioning

confidence: 99%

Total factor productivity of China’s marine economy: A meta-analysis

Dong

Qiao²,

Yuan³

et al. 2022

Front. Mar. Sci.

View full text Add to dashboard Cite

show abstract

“…Chen et al (2021) employed the three-stage DEA method to investigate agricultural total factor productivity. Huang et al (2022)measured and tested the robustness of China's agricultural total factor productivity using two different DEA models.…”

Section: Literature Reviewmentioning

confidence: 99%

Spatiotemporal characteristics and influencing factors of agricultural low-carbon economic efficiency in china

Xiao

Zhang

et al. 2022

Front. Environ. Sci.

View full text Add to dashboard Cite

Developing low-carbon agriculture can effectively avoid the waste of natural resources, thus contributing to the long-term sustainability of agriculture. This study uses the Super-SBM model to measure agricultural low-carbon economic efficiency (ALEE) in China from 2000 to 2018, then analyzes the spatial-temporal evolution characteristics. Simultaneously, the influencing factors of ALEE are investigated using spatial econometric model. The results show that: (1) In terms of temporal evolution, the ALEE in most provinces is declined over time, with only a few provinces improving. The ALEE in China and the three regions all show an obvious “L” trend of decline first and then stability. (2) From the perspective of spatial differentiation, provinces in eastern region have higher ALEE, while those in central and western regions have lower ALEE. Hainan’s ALEE has an absolute advantage, while Shanxi is the worst. (3) China’s ALEE shows obvious spatial agglomeration characteristics of H-H and L-L agglomeration, which are further enhanced over time. The number of L-L agglomeration provinces gradually increases, indicating that China’s ALEE has not been improved significantly. (4) Economic growth level and Agricultural scientific and Technological progress have effectively improved the ALEE. However, Capital deepening, Government fiscal expenditure, Agricultural planting structure, and Agricultural disaster all have negative impacts. Rural electricity consumption also has a negative impact, but the impact is not significant. To accelerate the development of low-carbon agriculture, all regions must not only pursue a differentiated low-carbon agriculture development path, but also accelerate agricultural transformation, strengthen research and development, and popularize low-carbon agricultural technologies, reduce the input of traditional agricultural means of production, optimize the agricultural industrial structure, and adjust agricultural subsidy policies.

show abstract

“…Chen Yanling et al (2022) used the recent 15-year provincial panel SBM-ML index method to measure agricultural productivity from the perspective of environmental constraints with agricultural surface source pollution as a non-desired output, and a dynamic panel regression model was used to empirically analyse the factors affecting agricultural productivity [11]. Huang Xiuquan et al (2022) constructed two different data envelopment analysis models, combining the green Luenberger productivity indicator (GLPI), a two-year weight-corrected Russell model, and a two-year bounded adjustment model to measure AGTFP in China and decompose AGTFP growth at both the production and factor levels to examine its drivers [12]. Based on panel data from 2001 to 2019 for 30 Chinese companies, Zhu Yingyu et al (2022) measured the green total factor productivity of China's plantation industry based on the net carbon sink using stochastic frontier analysis with an output-oriented distance function, and empirically investigated the impact of agricultural mechanisation on the green total factor productivity [13].…”

Section: Introductionmentioning

confidence: 99%

A Study of the Spatial Structure and Regional Interaction of Agricultural Green Total Factor Productivity in China Based on SNA and VAR Methods

Chen

Zhu

Sun³

et al. 2022

Sustainability

View full text Add to dashboard Cite

As regional interaction increases in an open economy, a region’s green total factor productivity in agriculture must be considered alongside relationships with other regions. In this study, the slack-based model (SBM) global Malmquist–Luenberger (GML) index is used to measure the green total factor productivity of agriculture in each province of China, and the social network analysis (SNA) and vector autoregressive model (VAR) impulse response function (IRF) are used to examine the spatial network structure and regional interactivity. The research confirms that the absolute value and concentration of agricultural green total factor productivity are generally higher in the south than in the north of China, but the peak is lower in the south than in the north. The network density of agricultural green total factor productivity in China from 2008 to 2019 shows an increase, with the cut-off values of mean, 10, 50, and 100 treated as 4.97%, 2.57%, 3.30%, and 2.43%, respectively. From 2008 to 2019, the central potentials of network entry and network exit of green total factor productivity in China’s agriculture show a “V”-shaped and inverted “V”-shaped evolution path, respectively, with the density of cohesive subgroups growing, which demonstrates that the spatial structure of green total factor productivity in Chinese agriculture has experienced an evolutionary path from polycentric to monocentric to polycentric conditions. The spatial interaction of different cohesive subgroups is intensifying and has a certain degree of self-stability. In terms of regional interaction, the siphon effect of the east on the green development of agriculture in the central and western regions is significant, but the trickle-down effect is not obvious, and the interaction between the central and western regions has a catalytic effect on the efficiency of the green economy of agriculture in both regions. It is recommended that targeted policies be introduced to support the flow of agricultural factors and industrial division of labour between the central and western regions and the south and north, taking into account the actual situation. The novelty of this paper is that it focuses on the green total factor productivity of Chinese agriculture and combines the innovative use of the social network analysis paradigm to analyse the green development of agriculture in a country from a spatial dynamic evolutionary perspective. A limitation of the research methodology in this paper is its poor applicability to closed economy analysis.

show abstract

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

Cited by 120 publications

References 64 publications

Total factor productivity of China’s marine economy: A meta-analysis

Total factor productivity of China’s marine economy: A meta-analysis

Spatiotemporal characteristics and influencing factors of agricultural low-carbon economic efficiency in china

A Study of the Spatial Structure and Regional Interaction of Agricultural Green Total Factor Productivity in China Based on SNA and VAR Methods

Contact Info

Product

Resources

About