29th AIM International Workshop –September, 2023

How Digital Transformation Promotes China's Energy Transition: An Empirical Study Based on a Quasi-experiment
Tu Ya-si1
1. Institute of Energy Research, Jiangxi Academy of Sciences, Nanchang, China

Introduction• Having become the world’s largest energy consumer and carbon emitter over the past few decades, China urgently needs totransition towards a more sustainable and green energy system.
• The digital transformation brought about by the emerging technologies is not only about the application of technology, but also aprofound process of change.
• Though digital transformation theoretically seems to be beneficial for energy transformation, further research and verification areneeded to evaluate its effectiveness in practical applications, particularly in energy transition.
• Empirical evidence in the context of a major country like China will be of great value for both policy-making and practicalimplementation.

Methodology• Research Plan
This study plans a quasi-experiment design and to form a panel data from 286 prefecture-level cities in China to estimate the causal
effects of digital transformation on sectoral energy consumption.
• Research Framework:
To thoroughly investigate the causal impact of digital transformation on China’s energy transition, we mainly employ the Differencein-Differences (DiD) method. This approach capitalizes on comparing changes over time between groups that are exposed to thedigital transformation and those that are not, allowing us to infer causal effects amidst potential confounding factors.
• Data Sources:

Theoretical Foundation• Solow Growth Model
The Solow growth model in economics assumes that the marginal returns
to investment are diminishing, meaning that beyond a certain level,
producing more results in lower efficiency. In the long run, technologicalprogress is the only source of economic growth. This theory lays theeconomic rationale and foundation for energy transition, as it implies thatcontinuing reliance on conventional energy sources with diminishingreturns necessitates shifting towards technological progress and innovationin renewable energy to sustain long-term growth.
• System Dynamics Model
The system dynamics model of digitalization and energy efficiency considers the interactions between various factors in complex
systems. This model can be utilized to describe how digital technologies can optimize energy utilization. Its widespread
application has led to the development of many theoretical frameworks on the relationship between digitalization and energy
efficiency, which have provided valuable insights for the mechanism analysis in this study.
• Technology Acceptance Model (TAM) theory
Beyond facilitating evaluations of acceptance, the intrinsic value of the digitalization Technology Acceptance Model in energy
transition research lies in its capability to identify barriers and drivers. This feature positions it as one of the dependable sources
for indicator development.

The panel data from 286 prefecture-level cities in China utilized for this research predominantly originate from official Chinese
statistical repositories, complemented by information from international energy and technological research institutions. This
encompasses data concerning energy consumption, modes of production, and the prevalence and application of digital technologies
across various provinces in China. (Data collection is in progress)• Model Specification:
We set forth a DiD econometric model wherein the dependent variable signifies indicators of energy transition progress acrossprovinces, and the independent variables encapsulate the extent and rate of digital technology applications. To account for otherpotential confounders, a series of control variables such as provincial GDP, per capita income, and industrial structures have been
incorporated.
• Causal Inference:
Leveraging a quasi-experimental design, we identify certain instrumental variables besides the DiD method to aid in establishing thecausal linkage between digital transformation and energy transition. For instance, sudden surges in digital investments within certain
provinces over specific timeframes are considered as instrumental variables, addressing potential endogeneity concerns.
• Statistical Analysis:
A battery of statistical tools, encompassing OLS regression, 2SLS, and fixed-effects models, have been planned for our statisticalanalyses. These instruments aim to extract meaningful insights from the data, anchoring the relationship between digitalization andenergy transition.
• Robustness Tests:
To ensure the robustness of the output, we plan to undertake various robustness checks. These encompass modifications in model
specifications, using different data subsets, and exploring alternative statistical approaches. In particular, parallel trends assumption,
alternative control groups, alternative time windows, placebo tests, heterogeneous treatment effects would be employed.

Analysis• Baseline Regression Model��� = � + � ⋅ ������ + � ⋅ ����� + � ⋅ (������ × ����� ) + �′�� � + ���

Where：
��� : Represents the energy transition indicator for city i in period t������ : A dummy variable equal to 1 for cities that underwent digital transformation and 0 otherwise.
����� ：A dummy variable equal to 1 for periods after the treatment and 0 otherwise.
������ × ����� : The interaction term. The coefficient δ provides our DiD estimate of interest.
�′�� � : A set of control variables, e.g., city GDP, per capita income, etc.
��� : The error term.
• Parallel Trends Test Model:��� = � + � ⋅ ������ +��� (������ × ��������� ) + �′�� � + ���Where：��������� : Refers to a series of dummy variables pointing to each pre-treatment period.
�� : The coefficients associated with each interaction term ��������� ​. For the parallel trends assumption to hold, we expect these coefficients to be statistically insignificant.

Discussion• The study has potential to conclude that digital transformation significantly reduces the energy intensity of China’s industrial,
transportation, and service sectors, with the largest effect observed in the industrial sector. Results from certain robustness tests address
peripheral questions pertaining to this research intention. The study may suggest that digital transformation is becoming an importantdriver for promoting China’s energy structure optimization and green low-carbon transition.
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