TFP Electricity Consumption Efficiency and Influencing Factor Analysis Based on DEA Method

Shu, Tianjia; Zhong, Xiao; Zhang, Shuguang

doi:10.1016/j.egypro.2011.10.013

Cited by 17 publications

(5 citation statements)

References 1 publication

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“…Zhang [35], Ladu and Meleddu [36], Shao et al [37], Shu et al [38], Liu and Liu [39], Li [40], and Zhang [33] used data from different regions for their studies; further, the first six of these authors considered both time and regional factors and used panel data for their analyses. Most of these authors argued that technological innovation is becoming a means of ensuring efficient energy use and energy savings.…”

Section: How Is Technological Innovation Measured?mentioning

confidence: 99%

What Is the Relationship between Technological Innovation and Energy Consumption? Empirical Analysis Based on Provincial Panel Data from China

2018

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This paper investigates the relationship between energy consumption and technological innovation using a dynamic panel data model and regional-level data from China for the period 1995-2012. In contrast to previous studies, it examines the short and long-run bilateral relationship between technological innovation and energy consumption. The results show that in the short run, technological innovation leads to an increase in energy consumption, while energy consumption has no significant effect on technological innovation. In the long run, however, energy consumption is positively and bilaterally related to technological innovation. These findings suggest that although technological innovation does not directly lead to a reduction in energy consumption as mentioned in the extant literature, it could help achieve sustainability through improving energy efficiency and developing energy structure for developing countries.

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Section: How Is Technological Innovation Measured?mentioning

confidence: 99%

What Is the Relationship between Technological Innovation and Energy Consumption? Empirical Analysis Based on Provincial Panel Data from China

2018

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“…One is the measurement method of bias in technological progress. The mainstream method is the stochastic frontier analysis method (Estache, 2004;Shu, 2011), which measures TFP based on the DEA index and further decomlocates technological change. It can decomlocates IBTC, OBTC, and MATC from technological change.…”

Section: Literature Reviewmentioning

confidence: 99%

Research on the Heterogeneity of Green Biased Technology Progress in Chinese Industries: Decomposition Index Analysis Based on the Slacks-based measure integrating

Meng

Liu

et al. 2022

JEA

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Green biased technological progress takes into account the influence of energy input and pollution emission, which is of great significance to China's green development. This paper decomposes technological progress into green input biased technological progress (IBTC) and green output biased technological progress (OBTC) using the Slacks-based measure integrating (SBM) model. Factor bias in technological progress is determined based on data from 34 industries in China from 2000 to 2015. The results show that the green biased technology progress exists significantly in the industry, and most of them promote the growth of green total factor productivity. IBTC first tends to consume energy to pursue capital between capital input and energy input, while it tends to save energy after the Eleventh Five-Year Plan. Between labor input and energy input, it is biased towards saving labor and consume resources. OBTC is biased towards promoting industrial growth and curbing pollution emissions. Medium and light polluting industries are biased towards promoting industrial growth and curbing pollution emissions, while heavy polluting industries are biased towards emitting more pollution.

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“…Model analisis DEA dengan asumsi CRS dan VRS digunakan untuk menentukan kecendrungan tren pada provinsi penerima DAK bidang ESK Tahun 2017 tergolong pada increasing return to scale (IRS), decreasing return to scale (DRS) atau constant return to scale (CRS). Menurut (Shu, Zhong and Zhang, 2011) Hasil olah data DEA dengan menggunakan asumsi CRS terdapat 8 provinsi dari 17 provinsi yang dinyatakan efisien dalam menggunakan Dana Alokasi Khusus Bidang Energi Skala Kecil tahun 2017 yang artinya sebanyak 8 provinsi telah melaksanakan kegiatan secara efisien dengan menggunakan masukan secara baik untuk menghasilkan keluaran yang maksimum. Sedangkan Provinsi yang dinilai tidak efisien antara lain Provinsi Riau, Lampung, Kepulauan Riau, Kalimantan Barat, Kalimantan Utara, Kalimantan Timur, Kalimantan Selatan, Sulawesi Barat dan Maluku Utara.…”

Section: Metodeunclassified

Analisis Kinerja Dana Alokasi Khusus Bidang Energi Skala Kecil dalam Meningkatkan Desa Berlistrik

2021

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Berdasarkan data yang disusun/dirilis oleh Kementerian ESDM, rasio elektrifikasi di Indonesia baru mencapai 95% di tahun 2017 dan sebanyak 5% rumah tangga di Indonesia masih belum mendapatkan akses ke pelayanan penyediaan tenaga listrik. Untuk mengatasi permasalahan tersebut, salah satu upaya pemerintah yang dilakukan yaitu dengan memberikan fasilitas pendanaan infrastuktur melalui program Dana Alokasi Khusus bidang energi skala kecil. Namun, dalam pelaksanaannya, diduga pelaksanaan DAK bidang energi skala kecil tersebut belum berjalan secara efisien. Dari target yang telah ditetapkan oleh pemerintah, terlihat bahwa serapan anggaran, peningkatan jumlah desa berlistrik, peningkatan jumlah kapasitas pembangkit masih belum optimal. Oleh sebab itu, penelitian ini dilakukan dengan tujuan yaitu untuk menganalisis kinerja pelaksanaan DAK bidang energi skala kecil. Penelitian ini mencakup kinerja 17 provinsi di Indonesia sebagai pelaksana DAK bidang energi skala kecil tahun 2017. Metode analisis yang digunakan yaitu Data Envelopment Analysis (DEA). Berdasarkan hasil yang didapat, ditemukan bahwa hanya 8 provinsi yang melaksanakan DAK bidang energi skala kecil dengan efisien. Hasil analisis DEA menunjukkan bahwa variabel ouput yang dapat ditingkatkan antara lain rumah tangga berlistrik, kapasitas terpasang pada masing-masing rumah dan desa berlistrik.

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TFP Electricity Consumption Efficiency and Influencing Factor Analysis Based on DEA Method

Cited by 17 publications

References 1 publication

What Is the Relationship between Technological Innovation and Energy Consumption? Empirical Analysis Based on Provincial Panel Data from China

What Is the Relationship between Technological Innovation and Energy Consumption? Empirical Analysis Based on Provincial Panel Data from China

Research on the Heterogeneity of Green Biased Technology Progress in Chinese Industries: Decomposition Index Analysis Based on the Slacks-based measure integrating

Analisis Kinerja Dana Alokasi Khusus Bidang Energi Skala Kecil dalam Meningkatkan Desa Berlistrik

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