Research on the Impact of Data Elementalization on Low-Carbon Economic Development
Journal: Modern Economics & Management Forum DOI: 10.32629/memf.v7i1.4917
Abstract
Under the "dual-carbon" goals background, data elementalization is regarded as a key force in promoting green economic transformation and achieving low-carbon development. In-depth research on its specific role holds significant theoretical and practical importance. Based on panel data from 30 provinces in China from 2012 to 2022, this paper constructs an econometric model to empirically examine the impact of data elementalization on the development level of the low-carbon economy. The study finds that data elementalization can significantly reduce carbon emission intensity and effectively enhance carbon total factor productivity. This positive promoting effect remains robust after endogeneity testing. Therefore, full attention should be given to the integration of data elements with the real economy to leverage their critical role in advancing green and low-carbon transformation.
Keywords
data elementalization, low-carbon economy, carbon emission intensity, carbon total factor productivity
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[1]Farboodi M, Veldkamp L. Long-run growth of financial data technology[J]. American Economic Review, 2020, 110(8): 2485-2523.
[2]Cai Yuezhou, Ma Wenjun. Impact of data factors on high-quality development and constraints on data flow[J]. The Journal of Quantitative & Technical Economics, 2021, 38(03): 64-83.
[3]Xu Xiang, Tian Xiaoxuan, Li Keaobo, et al. Scale estimation and structural analysis of China’s data factors: From the perspective of information value chain[J]. Contemporary Finance & Economics, 2024, (04): 3-16.
[4]Liu Taoxiong, Rong Ke, Zhang Yadi. Estimation of data capital and its contribution to China’s economic growth: Based on the data value chain perspective[J]. Social Sciences in China, 2023, (10): 44-64+205.
[5]Farboodi M, Veldkamp L. A model of the data economy[R]. Cambridge, MA, USA: National Bureau of Economic Research, 2021.
[6]Acemoglu D, Restrepo P. Automation and new tasks: How technology displaces and reinstates labor[J]. Journal of economic perspectives, 2019, 33(2): 3-30.
[7]Li Li, Yong Hui, Wang Lei, et al. Data factor embedding, dual innovation and manufacturing transformation and upgrading[J]. Journal of Statistics and Information, 2024, 39(04): 31-45.
[8]Yang Yan, Wang Li, Liao Zujun. Market-oriented allocation of data factors and regional economic development: Based on the perspective of data trading platform[J]. Social Science Research, 2021, (06): 38-52.
[9]Chen Xiaojia, Xu Wei. Data factors, transportation infrastructure and industrial structure upgrading: Analysis based on quantitative spatial general equilibrium model[J]. Management World, 2024, 40(04): 78-98.
[10]Shi Dan, Sun Guanglin. Data factors and new quality productive forces: From the perspective of enterprise total factor productivity[J]. Economic Theory and Business Management, 2024, 44(04): 12-30.
[11]Shan Haojie. Reestimating the capital stock of China: 1952–2006[J]. The Journal of Quantitative & Technical Economics, 2008, 25(10): 17-31.
[12]Cong Jianhui, Liu Xuemin, Zhao Xueru. Boundary definition and measurement methods of urban carbon emission accounting[J]. China Population, Resources and Environment, 2014, 24(04): 19-26.
[13]Zhang Liao, Hu Zhongbo. Research on the impact of data factorization on common prosperity[J]. Soft Science, 2024, 38(11): 18-25+33.
[14]Huang Qunhui, Yu Yongze, Zhang Songlin. Internet development and manufacturing productivity improvement: Internal mechanism and China’s experience[J]. China Industrial Economics, 2019, (08): 5-23.
[2]Cai Yuezhou, Ma Wenjun. Impact of data factors on high-quality development and constraints on data flow[J]. The Journal of Quantitative & Technical Economics, 2021, 38(03): 64-83.
[3]Xu Xiang, Tian Xiaoxuan, Li Keaobo, et al. Scale estimation and structural analysis of China’s data factors: From the perspective of information value chain[J]. Contemporary Finance & Economics, 2024, (04): 3-16.
[4]Liu Taoxiong, Rong Ke, Zhang Yadi. Estimation of data capital and its contribution to China’s economic growth: Based on the data value chain perspective[J]. Social Sciences in China, 2023, (10): 44-64+205.
[5]Farboodi M, Veldkamp L. A model of the data economy[R]. Cambridge, MA, USA: National Bureau of Economic Research, 2021.
[6]Acemoglu D, Restrepo P. Automation and new tasks: How technology displaces and reinstates labor[J]. Journal of economic perspectives, 2019, 33(2): 3-30.
[7]Li Li, Yong Hui, Wang Lei, et al. Data factor embedding, dual innovation and manufacturing transformation and upgrading[J]. Journal of Statistics and Information, 2024, 39(04): 31-45.
[8]Yang Yan, Wang Li, Liao Zujun. Market-oriented allocation of data factors and regional economic development: Based on the perspective of data trading platform[J]. Social Science Research, 2021, (06): 38-52.
[9]Chen Xiaojia, Xu Wei. Data factors, transportation infrastructure and industrial structure upgrading: Analysis based on quantitative spatial general equilibrium model[J]. Management World, 2024, 40(04): 78-98.
[10]Shi Dan, Sun Guanglin. Data factors and new quality productive forces: From the perspective of enterprise total factor productivity[J]. Economic Theory and Business Management, 2024, 44(04): 12-30.
[11]Shan Haojie. Reestimating the capital stock of China: 1952–2006[J]. The Journal of Quantitative & Technical Economics, 2008, 25(10): 17-31.
[12]Cong Jianhui, Liu Xuemin, Zhao Xueru. Boundary definition and measurement methods of urban carbon emission accounting[J]. China Population, Resources and Environment, 2014, 24(04): 19-26.
[13]Zhang Liao, Hu Zhongbo. Research on the impact of data factorization on common prosperity[J]. Soft Science, 2024, 38(11): 18-25+33.
[14]Huang Qunhui, Yu Yongze, Zhang Songlin. Internet development and manufacturing productivity improvement: Internal mechanism and China’s experience[J]. China Industrial Economics, 2019, (08): 5-23.
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