The Chinese government is committed to achieve the goal of “double carbon” and proposes to shift from double control of energy consumption to double control of carbon emissions. In this scenario, it is of great theoretical and practical significance to study the impact of renewable energy transformation (RET) and technological innovation on carbon productivity (CP). Based on panel data obtained from 30 provinces of China from 2004 to 2021, this study empirically investigated the influence of RET and technological innovation on CP by using panel mean group (MG) estimation. For robustness test, ordinary least squares estimation method was adopted. The main conclusions are as follows: First, based on MG estimation, it was observed that RET has significant positive impact on CP in China. However, the coefficient of technological innovation was found to be significantly negative, indicating that enhancing technological innovation can improve CP. Additionally, the findings showed that economic development and industrial upgradation had a positive impact on CP. Second, the heterogeneity study showed that the RET in the eastern and western regions of China can improve CP. The coefficient of RET in the western region was significantly higher than that in the eastern region. The technological innovation coefficients in the eastern and central regions were significantly positive and enhancing technological innovation in these two regions can considerably improve CP; the technological innovation coefficient in the eastern region was higher than that in the central region. The Gross Domestic Product (GDP) coefficients of the three regions were significantly positive and enhancing economic development can increase CP in these three regions. Finally, to improve CP, it is suggested to promote RET, increase investment in research and development, enhance technological innovation, emphasize high-quality development, prioritize adapting to local conditions, and implement region-appropriate policies and measures.

1.
H.
Pei
and
J.
Chen
, “
Research on the relationship between technological innovation and carbon productivity based on provincial panel data
,”
Econ. Manage. Rev.
4
,
109
118
(
2023
) (in Chinese).
2.
X.
Liu
,
Q.
Niu
,
S.
Dong
, and
S.
Zhong
, “
How does renewable energy consumption affect carbon emission intensity? Temporal-spatial impact analysis in China
,”
Energy
284
,
128690
(
2023
).
3.
R.
Li
,
Q.
Wang
, and
L.
Li
, “
Does renewable energy reduce per capita carbon emissions and per capita ecological footprint? New evidence from 130 countries
,”
Energy Strategy Rev.
49
,
101121
(
2023
).
4.
T. H.
Le
, “
Connectedness between nonrenewable and renewable energy consumption, economic growth and CO2 emission in Vietnam: New evidence from a wavelet analysis
,”
Renewable Energy
195
,
442
454
(
2022
).
5.
Q.
Zaman
,
Z.
Wang
,
S.
Zaman
, and
S. F.
Rasool
, “
Investigating the nexus between education expenditure, female employers, renewable energy consumption and CO2 emission: Evidence from China
,”
J. Cleaner Prod.
312
,
127824
(
2021
).
6.
X. X.
Liu
,
Z. H.
Wang
,
X.
Sun
,
L.
Zhang
, and
M.
Zhang
, “
Clarifying the relationship among clean energy consumption, haze pollution and economic growth-based on the empirical analysis of China's Yangtze River Delta Region
,”
Ecol. Complex
44
,
100871
(
2020
).
7.
W. S.
Zhang
,
Y.
Xu
,
C.
Wang
, and
D. G.
Streets
, “
Assessment of the driving factors of CO2 mitigation costs of household biogas systems in China: A LMDI decomposition with cost analysis model
,”
Renewable Energy
181
,
978
989
(
2022
).
8.
Y.
Hao
,
Z. Y.
Zhang
,
C. X.
Yang
, and
H. T.
Wu
, “
Does structural labor change affect CO2 emissions? Theoretical and empirical evidence from China
,”
Technol. Forecast. Soc. Change
171
,
120936
(
2021
).
9.
Y.
Yu
,
C.
Shi
,
J.
Guo
,
Q.
Pang
,
M.
Deng
, and
X.
Na
, “
To what extent can clean energy development advance the carbon peaking process of China
,”
J. Cleaner Prod.
412
,
137424
(
2023
).
10.
X.
Liu
,
S.
Zhang
, and
B.
Junghan
, “
Nonlinear analysis of technological innovation and electricity generation on carbon dioxide emissions in China
,”
J. Cleaner Prod.
343
,
131021
(
2023
).
11.
C.
Cheng
,
X.
Ren
,
K.
Dong
,
X. M.
Dong
, and
Z.
Wang
, “
How does technological innovation mitigate CO2 emissions in OECD countries? Heterogeneous analysis using panel quantile regression
,”
J. Environ. Manage.
280
,
111818
(
2021
).
12.
C.
Chen
and
C.
Lee
, “
Does technological innovation reduce CO2 emissions? Cross-country evidence
,”
J. Cleaner Prod.
263
,
121550
(
2020
).
13.
K.
Zaman
and
M. A.
Moemen
, “
Energy consumption, carbon dioxide emissions and economic development: Evaluating alternative and plausible environmental hypothesis for sustainable growth
,”
Renewable Sustainable Energy Rev.
74
,
1119
1130
(
2017
).
14.
B.
Ozcan
,
P. G.
Tzeremes
, and
N. G.
Tzeremes
, “
Energy consumption, economic growth and environmental degradation in OECD countries
,”
Econ. Modell.
84
,
203
213
(
2020
).
15.
M.
Ahmad
,
Z.
Ahmed
,
A.
Majeed
, and
B.
Huang
, “
An environmental impact assessment of economic complexity and energy consumption: Does institutional quality make a difference?
,”
Environ. Impact Assess. Rev.
89
,
106603
(
2021
).
16.
U. K.
Pata
, “
Renewable and non-renewable energy consumption, economic complexity, CO2 emissions, and ecological footprint in the USA: Testing the EKC hypothesis with a structural break
,”
Environ. Sci. Pollut. Res.
28
,
846
861
(
2021
).
17.
F. J.
Hasanov
,
S.
Mukhtarov
, and
E.
Suleymanov
, “
The role of renewable energy and total factor productivity in reducing CO2 emissions in Azerbaijan. Fresh insights from a new theoretical framework coupled with Autometrics
,”
Energy Strategy Rev.
47
,
101079
(
2023
).
18.
F.
Chien
,
C. C.
Hsu
,
I.
Ozturk
,
A.
Sharif
, and
M.
Sadiq
, “
The role of renewable energy and urbanization towards greenhouse gas emission in top Asian countries: Evidence from advance panel estimations
,”
Renewable Energy
186
,
207
216
(
2022
).
19.
F.
Bilgili
,
E.
Koçak
, and
Ü.
Bulut
, “
The dynamic impact of renewable energy consumption on CO2 emissions: A revisited environmental Kuznets curve approach
,”
Renewable Sustainable Energy Rev.
54
,
838
845
(
2016
).
20.
P.
Behera
,
A.
Haldar
, and
N.
Sethi
, “
Achieving carbon neutrality target in the emerging economies: Role of renewable energy and green technology
,”
Gondwana Res.
121
,
16
32
(
2023
).
21.
Y.
Sun
,
H.
Li
,
Z.
Andlib
, and
M. G.
Genie
, “
How do renewable energy and urbanization cause carbon emissions? Evidence from advanced panel estimation techniques
,”
Renewable Energy
185
,
996
1005
(
2022
).
22.
M. M.
Rahman
,
K.
Alam
, and
E.
Velayutham
, “
Reduction of CO2 emissions: The role of renewable energy, technological innovation and export quality
,”
Energy Rep.
8
,
2793
2805
(
2022
).
23.
X.
Zhou
,
M.
Jia
,
M.
Altuntas
,
D.
Kirikkaleli
, and
M.
Hussain
, “
Transition to renewable energy and environmental technologies: The role of economic policy uncertainty in top five polluted economies
,”
J. Environ. Manage.
313
,
115019
(
2022
).
24.
M.
Ahmad
,
Z.
Ahmed
,
M.
Riaz
, and
X.
Yang
, “
Modeling the linkage between climate-tech, energy transition, and CO2 emissions: Do environmental regulations matter?
,”
Gondwana Res.
127
,
131
143
(
2023
).
25.
M. B.
Jebli
and
S. B.
Youssef
, “
The role of renewable energy and agriculture in reducing CO2 emissions: Evidence for North Africa countries
,”
Ecol. Indic.
74
,
295
301
(
2017
).
26.
A.
Raihan
and
A.
Tuspekova
, “
Toward a sustainable environment: Nexus between economic growth, renewable energy use, forested area, and carbon emissions in Malaysia
,”
Resour. Conserv. Recycl. Adv.
15
,
200096
(
2022
).
27.
T.
Fatima
,
U.
Shahzad
, and
L.
Cui
, “
Renewable and nonrenewable energy consumption, trade and CO2 emissions in high emitter countries: Does the income level matter?
,”
J. Environ. Plann. Manage.
64
,
1227
1251
(
2021
).
28.
S.
Shafiei
and
R. A.
Salim
, “
Non-renewable and renewable energy consumption and CO2 emissions in OECD countries: A comparative analysis
,”
Energy Policy
66
,
547
556
(
2014
).
29.
S.
Meng
,
R.
Sun
, and
F.
Guo
, “
Does the use of renewable energy increase carbon productivity?—An empirical analysis based on data from 30 provinces in China
,”
J. Cleaner Prod.
365
,
132647
(
2022
).
30.
M.
Murshed
,
N.
Apergis
,
M. S.
Alam
,
U.
Khan
, and
S.
Mahmud
, “
The impacts of renewable energy, financial inclusivity, globalization, economic growth, and urbanization on carbon productivity: Evidence from net moderation and mediation effects of energy efficiency gains
,”
Renewable Energy
196
,
824
838
(
2022
).
31.
F. J.
Hasanov
,
Z.
Khan
,
M.
Hussain
, and
M.
Tufail
, “
Theoretical framework for the carbon emissions effects of technological progress and renewable energy consumption
,”
Sustainable Dev.
29
,
810
822
(
2021
).
32.
C.
Raghutla
and
K. R.
Chittedi
, “
The effect of technological innovation and clean energy consumption on carbon neutrality in top clean energy-consuming countries: A panel estimation
,”
Energy Strategy Rev.
47
,
101091
(
2023
).
33.
J. Y.
Mo
, “
Technological innovation and its impact on carbon emissions: Evidence from Korea manufacturing firms participating emission trading scheme
,”
Technol. Anal. Strategic Manage.
34
,
47
57
(
2022
).
34.
O. A.
Shobande
and
L.
Ogbeifun
, “
Pooling cross-sectional and time series data for estimating causality between technological innovation, affluence and carbon dynamics: A comparative evidence from developed and developing countries
,”
Technol. Forecast. Soc. Change
187
,
122192
(
2023
).
35.
Y. L.
Xu
,
M.
Umar
, and
M.
Altuntaş
, “
Carbon neutrality target in Turkey: Measuring the impact of technological innovation and structural change
,”
Gondwana Res.
109
,
429
441
(
2022
).
36.
X.
Chen
,
M. A.
Rahaman
,
M.
Murshed
,
H.
Mahmood
, and
M. A.
Hossain
, “
Causality analysis of the impacts of petroleum use, economic growth, and technological innovation on carbon emissions in Bangladesh
,”
Energy
267
,
126565
(
2023
).
37.
A. T.
Sunday
,
U.
Sami
,
K. M.
Tevfik
,
A.
Kishwar
,
P. U.
Korkut
, and
A.
Mehmet
, “
Endorsing sustainable development in BRICS: The role of technological innovation, renewable energy consumption, and natural resources in limiting carbon emission
,”
Sci. Total Environ.
859
,
160181
(
2023
).
38.
U.
Habiba
,
X.
Cao
, and
A.
Anwar
, “
Do green technology innovations, financial development, and renewable energy use help to curb carbon emissions?
,”
Renewable Energy
193
,
1082
1093
(
2022
).
39.
M. M.
Rahman
and
K.
Alam
, “
Effects of corruption, technological innovation, globalisation, and renewable energy on carbon emissions in Asian countries
,”
Utilities Policy
79
,
101448
(
2022
).
40.
L. W.
Fan
,
J.
You
,
W.
Zhang
, and
P.
Zhou
, “
How does technological progress promote carbon productivity? Evidence from Chinese manufacturing industries
,”
J. Environ. Manage.
277
,
111325
(
2021
).
41.
R.
Long
,
T.
Shao
, and
H.
Chen
, “
Spatial econometric analysis of China's province-level industrial carbon productivity and its influencing factors
,”
Appl. Energy
166
,
210
219
(
2016
).
42.
X.
Liu
and
X.
Zhang
, “
Industrial agglomeration, technological innovation and carbon productivity: Evidence from China
,”
Resour., Conserv. Recycl.
166
,
105330
(
2021
).
43.
J.
Huang
,
X.
Chen
,
K.
Yu
, and
X.
Cai
, “
Effect of technological progress on carbon emissions: New evidence from a decomposition and spatiotemporal perspective in China
,”
J. Environ. Manage.
274
,
110953
(
2020
).
44.
J.
Wei
,
S.
Rahim
, and
S.
Wang
, “
Role of environmental degradation, institutional quality and government health expenditures for human health: Evidence from emerging seven countries
,”
Front. Public Health
10
,
870767
(
2022
).
45.
L.
Zheng
,
L.
Yuan
,
Z.
Khan
,
R. A.
Badeeb
, and
L.
Zhang
, “
How G-7 countries are paving the way for net-zero emissions through energy efficient ecosystem?
,”
Energy Econ.
117
,
106428
(
2023
).
46.
B. S.
Atasoy
, “
Testing the environmental Kuznets curve hypothesis across the US: Evidence from panel mean group estimators
,”
Renewable Sustainable Energy Rev.
77
,
731
747
(
2017
).
47.
M. H.
Pesaran
and
T.
Yamagata
, “
Testing slope homogeneity in large panels
,”
J. Econometrics
142
,
50
93
(
2008
).
48.
M. H.
Pesaran
, “
General diagnostic tests for cross section dependence in panels
,” University of Cambridge & USC Discussion Paper No. 1240, June
2004
.
49.
Y.
Dou
,
J.
Zhao
,
X.
Dong
, and
K.
Dong
, “
Quantifying the impacts of energy inequality on carbon emissions in China: A household-level analysis
,”
Energy Econ.
102
,
105502
(
2021
).
50.
K.
Khalid
,
B.
Hussain
, and
S.
Ali
, “
Evaluating eco-efficiency in consumption and production through sustainable utilization of resources: A panel analysis of APAC by population
,”
Renewable Energy
170
,
1096
1106
(
2021
).
51.
M. H.
Pesaran
, “
A simple panel unit root test in the presence of cross-section dependence
,”
J. Appl. Econ.
22
,
265
312
(
2007
).
52.
M. F.
Bashir
,
M.
Shahbaz
,
M. N.
Malik
,
B.
Ma
, and
J.
Wang
, “
Energy transition, natural resource consumption and environmental degradation: The role of geopolitical risk in sustainable development
,”
Resour. Policy
85
,
103985
(
2023
).
53.
J.
Westerlund
, “
Testing for error correction in panel data
,”
Oxford Bull. Econ. Stat.
69
,
709
748
(
2007
).
54.
Q.
Ma
,
M.
Tariq
,
H.
Mahmood
, and
Z.
Khan
, “
The nexus between digital economy and carbon dioxide emissions in China: The moderating role of investments in research and development
,”
Technol. Soc.
68
,
101910
(
2022
).
55.
I.
Shahzadi
,
M.
Yaseen
,
M.
Khan
,
M.
Makhdum
, and
Q.
Ali
, “
The nexus between research and development, renewable energy and environmental quality: Evidence from developed and developing countries
,”
Renewable Energy
190
,
1089
1099
(
2022
).
56.
M. H.
Pesaran
and
R.
Smith
, “
Estimating long-run relationships from dynamic heterogeneous panels
,”
J. Econometrics
68
,
79
113
(
1995
).
57.
S.
Kusairi
,
S.
Muhamad
,
M.
Musdholifah
, and
S. C.
Chang
, “
Labor market and household debt in Asia Pacific countries: Dynamic heterogeneous panel data analysis
,”
J. Int. Commer. Econ. Policy
10
,
1950011
(
2019
).
58.
M. H.
Pesaran
,
Y.
Shin
, and
R. P.
Smith
, “
Pooled mean group estimation of dynamic heterogeneous panels
,”
J. Am. Stat. Assoc.
94
,
621
634
(
1999
).
59.
J.
Huang
, “
Research on generalized wealth effects of real estate price rising in China
,” Ph.D. dissertation (Shanghai Jiao Tong University,
2010
) (in Chinese).
60.
Y.
Kaya
and
K.
Yokobori
,
Environment, Energy and Economy: Strategies for Sustainability
(
United Nations University Press
,
1997
).
61.
S. L.
Lin
,
Z.
Zhang
, and
G.
Liu
, “
Technological innovation, spatial agglomeration and regional carbon productivity, China Population
,”
Resour. Environ.
23
,
36
45
(
2013
) (in Chinese).
62.
X. H.
Liu
,
T.
Zhao
,
C.-T.
Chang
, and
C. J.
Fu
, “
China's renewable energy strategy and industrial adjustment policy
,”
Renewable Energy
170
,
1382
1395
(
2021
).
63.
S.
Ates
, “
Energy efficiency and CO2 mitigation potential of the Turkish iron and steel industry using the LEAP (long-range energy alternatives planning) system
,”
Energy
90
,
417
428
(
2015
).
64.
M.
Shahbaz
,
J.
Wang
,
K.
Dong
, and
J.
Zhao
, “
The impact of digital economy on energy transition across the globe: The mediating role of government governance
,”
Renewable Sustainable Energy Rev.
166
,
112620
(
2022
).
65.
Y.
Liu
,
X.
Dong
, and
K.
Dong
, “
Pathway to prosperity? The impact of low-carbon energy transition on China's common prosperity
,”
Energy Econ.
124
,
106819
(
2023
).
66.
S.
Xu
,
Y.
Zhang
,
L.
Chen
,
L. W.
Leong
,
I.
Muda
, and
A.
Ali
, “
How Fintech and effective governance derive the greener energy transition: Evidence from panel-corrected standard errors approach
,”
Energy Econ.
125
,
106881
(
2023
).
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