Evaluation of multi-period regional R&amp;D efficiency: An application of dynamic DEA to China's regional R&amp;D systems

Chen, Kaihua; Kou, Mingting; Fu, Xiaolan

doi:10.1016/j.omega.2017.01.010

Cited by 112 publications

(73 citation statements)

References 51 publications

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“…Our model showed that only a few provinces in eastern China are sustainably innovation efficient, while most provinces in central and western China have a medium, to low, to very low sustainable innovation efficiency. This is in line with previous research [19,21,27,101]. Eastern China always showed a higher innovation efficiency than central and western areas.…”

Section: Hypothesessupporting

confidence: 93%

“…Chen et al [27] measured the regional R&D and commercialization efficiencies for the high-tech industries in China through a network DEA method, and found that most of the Chinese regions had a low efficiency, while eastern China had a higher innovation efficiency than central and western China. Chen et al [101] developed a dynamic analytical framework of regional R&D efficiency systems, considering the dynamic interdependence between regional R&D activities over different periods, and they ascertained that eastern China had a higher efficiency than other provinces. To date, the relationship between sustainable innovation and innovation efficiency has been vague [70].…”

Section: Regional Sustainable Innovation Efficiency Evaluationmentioning

confidence: 99%

“…It is necessary to consider the existence of a time lag in the transformation procedure from R&D inputs to transform into outputs [121][122][123]. Yet, in the literature, no specific time lag length on innovation outputs has been generally accepted [101,124]. However, there is also no significant difference in the time lag span in innovation evaluation research [122,125,126].…”

Section: Data Sourcesmentioning

confidence: 99%

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Efficiency Evaluation of Regional Sustainable Innovation in China: A Slack-Based Measure (SBM) Model with Undesirable Outputs

Bossink

Chen

2019

Sustainability

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An efficiency evaluation of China's regional sustainable innovation, evaluating industrial waste and total energy consumption, is the main research subject in this paper. It focuses on a regional measurement and comparison of these undesirable outputs of Chinese firm activities, such as industrial SO 2 and CO 2 emissions. By applying a data envelopment analysis-slack-based measure (DEA-SBM) model with undesirable outputs indicators, the regional innovation efficiency was evaluated for 30 provinces in China, from 2002 to 2014. The results indicate that the sustainable innovation efficiency of overall China is still relatively low, and varies significantly in different regions. Central and Western China have similar sustainable innovation efficiencies, which are much lower than the sustainable innovation efficiency in Eastern China. Furthermore, the data indicate that regional sustainable innovation efficiency disparities among these three areas are decreasing. Based on these findings, reasons for the sustainable innovation efficiency gap among the different regions were analyzed. To scholars, this paper extends the research on regional sustainable innovation efficiency by implementing an undesirable output perspective to the DEA-SBM model. The findings also provide Chinese policy makers with useful decision support insights for regional sustainable innovation, and energy conservation and emission reduction policies.Sustainability 2020, 12, 31 2 of 21 differences can be addressed [15][16][17]. Research on regional innovation efficiency has gained increasing attention in recent years [18][19][20], but to the best of our knowledge, very few studies have been conducted to quantitatively evaluate sustainable innovation efficiency in China, by means of focusing on undesirable outputs like industrial SO 2 and CO 2 emissions [21,22]. Our study aims to contribute to the knowledge in this area, and does this by means of measuring and comparing unsustainable, unwanted, and undesirable outcomes/byproducts of Chinese regions' innovative firm activities. By evaluating the regional sustainable innovation efficiency of 30 provinces in China from 2002 to 2014, using the slack-based measure (SBM) model, this study focuses on the following research question: what are the differences between sustainable innovation efficiencies of different regions in China and which trends in these differences can be estimated?How to deal with SO 2 and CO 2 emissions and with related climate change has become one of the most urgent challenges of today's world. For example, it has been reported that global CO 2 emissions from fuel combustion has increased from 26,177 Mt in 2004 to 32,190 Mt in 2013. Many countries have committed to controlling SO 2 and CO 2 emissions and aim to develop a no-to low-emissions economy. In 2015, the Paris Climate Change Conference adopted the "Paris Agreement" to deal with climate change after 2020 [24]. More than 170 countries jointly signed the Paris Agreement in 2016. China and the United States officially ratified the...

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Section: Hypothesessupporting

confidence: 93%

Section: Regional Sustainable Innovation Efficiency Evaluationmentioning

confidence: 99%

Section: Data Sourcesmentioning

confidence: 99%

See 1 more Smart Citation

Efficiency Evaluation of Regional Sustainable Innovation in China: A Slack-Based Measure (SBM) Model with Undesirable Outputs

Bossink

Chen

2019

Sustainability

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“…It calculates and compares the relative efficiency of DMU by using a mathematical programming model and evaluates the DMU on this basis. As a widely used objective evaluation method, the DEA has been applied in various fields since it was proposed, such as technical efficiency [13], eco-efficiency [14], productive efficiency [15], performance of the real estate industry [16], and R&D in efficiency measurement [17].…”

Section: Data Envelopment Analysis (Dea)mentioning

confidence: 99%

“…Research and experimental development (R&D) personnel full-time equivalent. [17,28] Effective sunshine duration Number of effective hours of annual illumination calculated from optimum angle of incident light and radiation time. [44,45] Appendix C…”

Section: Randd In Electricity Industrymentioning

confidence: 99%

Efficiency Measurement and Factor Analysis of China’s Solar Photovoltaic Power Generation Considering Regional Differences Based on a FAHP–DEA Model

Yang

Wang

et al. 2020

Energies

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Driven by the transformation of the energy structure, China's photovoltaic (PV) power generation industry has made remarkable achievements in recent years. However, there are more than 30 regions (cities/provinces) in China, and the economic, policy, technological, and the environmental conditions of each region are significantly different, which leads to a huge discrepancy in PV power generation efficiency. To address the imbalance in the development of PV industry, first, this paper employed the integrated fuzzy analytic hierarchy process-data envelopment analysis (FAHP-DEA) model to evaluate the PV power generation efficiency of 30 regions in China. Second, Tobit regression model was used to examine the effects of 9 potential influencing factors. Third, a concrete analysis was conducted, and discussion based on the efficiency rankings and regression results was made. Additionally, the FAHP-DEA model proposed in this study can also be applied to the efficiency evaluation issues of other types of renewable energy.

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Agribusiness diversification and technological innovation efficiency: A U‐shaped relationship

Zhang

Sun

et al. 2022

Agribusiness

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In recent years, agribusiness firms have increasingly diversified their product portfolio and expanded into new business areas through internal growth or acquisition, which can lead to the reallocation of innovation resources. This study examines the impact of agribusiness diversification (AD) on technological innovation efficiency (TIE) in China. Based on an 8‐year longitudinal dataset of 101 Chinese listed agribusiness firms from 2012 to 2019, this study applies the Tobit regression technique to reveal the underlying mechanism between AD and TIE. The empirical results show that: (1) the AD‐TIE relationship follows a U‐shaped pattern, that is, at low levels of AD, the AD‐TIE relationship is negative; at high levels of AD, the AD‐TIE relationship is positive. (2) government subsidies (GS) negatively moderate the U‐shaped relationship between AD and TIE, such that GS flattens the U‐shaped curve. Our findings have important implications for agribusiness managers and public policymakers. [EconLit citations: H25, L25, Q16].

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Evaluation of multi-period regional R&D efficiency: An application of dynamic DEA to China's regional R&D systems

Cited by 112 publications

References 51 publications

Efficiency Evaluation of Regional Sustainable Innovation in China: A Slack-Based Measure (SBM) Model with Undesirable Outputs

Efficiency Evaluation of Regional Sustainable Innovation in China: A Slack-Based Measure (SBM) Model with Undesirable Outputs

Efficiency Measurement and Factor Analysis of China’s Solar Photovoltaic Power Generation Considering Regional Differences Based on a FAHP–DEA Model

Agribusiness diversification and technological innovation efficiency: A U‐shaped relationship

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