Bytes into watts: Input–output tracking of energy-sector digitalization and its decarbonization effect

跟踪(教育) 字节 可再生能源 工程类 计算机科学 功率(物理) 汽车工程 环境科学 电气工程 能量(信号处理) 跟踪系统 分布式发电 环境经济学 高效能源利用
作者
Baojun Tang,Junyu Chen,Changjing Ji,Yongji Zhang
出处
期刊:Energy Policy [Elsevier BV]
卷期号:212: 115157-115157 被引量:3
标识
DOI:10.1016/j.enpol.2026.115157
摘要

The digitalization of the energy sector is emerging as a key driver of deep urban decarbonization. Yet rigorously quantifying this digital shift—and therefore its true impact on emissions—remains challenging. To address the gap, we develop a Digital Inputs Technical Coefficient (DITC) drawn from an inter-city, multi-regional input–output table that differentiates enterprise ownership. Merging the DITC with panel data for 340 Chinese cities in 2002, 2007, 2012 and 2017, we investigate how digitalization influences carbon total-factor productivity (CTFP). Our estimates indicate that a one-standard-deviation increase in the DITC raises CTFP by roughly 0.63 pp. This effect operates through three channels: green technological innovation, more efficient factor allocation, and a cleaner energy mix. The emission-reduction multiplier for foreign enterprises (1.89) far exceeds that for domestic firms (0.83), pointing to amplification via technology spill-overs, competitive pressures, and stricter governance standards. Digitalization’s benefits also diverge sharply across regions: energy-poor cities gain far more than already-developed ones. Moreover, digital infrastructure exhibits a pronounced threshold effect—cities below a critical level see virtually no gains, whereas those above it experience strongly amplified benefits. Overall, the study offers new empirical evidence on measurement, mechanisms and heterogeneity in energy-sector digitalization, providing a solid basis for precisely targeted investments and equitable transition policies. While the analysis is centered on 340 Chinese cities (2002–2017), the identified mechanisms and heterogeneity offer transferable insights for other developing economies pursuing similar digital and energy transitions.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
FashionBoy的应助被sixo采纳,获得10
刚刚
1秒前
科研通AI6.2的应助被WNL采纳,获得10
1秒前
科目三的应助被乐观的鞋垫采纳,获得10
1秒前
2秒前
李健的小迷弟的应助被DueDue0327采纳,获得10
2秒前
3秒前
3秒前
含含含完成签到,获得积分10
3秒前
3秒前
3秒前
mk_smile发布了新的文献求助10
4秒前
qyy发布了新的文献求助10
4秒前
没有答案完成签到,获得积分10
4秒前
孤独的芒果完成签到,获得积分10
4秒前
弓长尔东的应助被忧伤的千山采纳,获得10
4秒前
tot完成签到 ,获得积分10
5秒前
勤奋的冰淇淋完成签到,获得积分10
5秒前
6秒前
畔畔发布了新的文献求助30
6秒前
yiyu完成签到 ,获得积分10
7秒前
ZZ发布了新的文献求助10
7秒前
小恩发布了新的文献求助10
7秒前
Lindsay发布了新的文献求助10
7秒前
丘比特的应助被郭郭郭采纳,获得10
7秒前
JJJ完成签到,获得积分10
8秒前
8秒前
狒狒发布了新的文献求助10
9秒前
FashionBoy的应助被滴滴滴采纳,获得10
9秒前
9秒前
9秒前
NeonFire完成签到,获得积分10
9秒前
liuruanruan完成签到,获得积分10
9秒前
FashionBoy的应助被158zzz采纳,获得10
10秒前
11秒前
11秒前
所所的应助被yyl采纳,获得10
11秒前
Owen的应助被Active采纳,获得10
12秒前
HXZ发布了新的文献求助10
13秒前
Shauna发布了新的文献求助30
13秒前
高分求助中
(应助此贴封号)通过应助OA文献获取积分 10000
Rosenblum, Global Change Biology 800
The Art of Interactive Teaching 600
Computational Chemical Reaction Engineering: Modeling, Simulation, and Design with MATLAB 600
Organizational Behavior 510
Management and the Arts 510
CLSI C56QG Examples of Hemolyzed, Icteric, and Lipemic/Turbid Samples Quick Guide 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 计算机科学 工程类 纳米技术 内科学 物理 有机化学 化学工程 生物化学 复合材料 光电子学 细胞生物学 心理学 量子力学 催化作用 物理化学 电极
热门帖子
关注 科研通微信公众号,转发送积分 7800252
求助须知:如何正确求助?哪些是违规求助? 9335103
关于积分的说明 20471898
捐赠科研通 7391820
什么是DOI,文献DOI怎么找? 3326337
关于科研通互助平台的介绍 2473222
邀请新用户注册赠送积分活动 2344061