Content uploaded by Ali Emrouznejad
Author content
All content in this area was uploaded by Ali Emrouznejad on Feb 03, 2025
Content may be subject to copyright.
Ai, H., X. Tan, S.K. Mangla, A. Emrouznejad, F. Liu, and M. Song (2025) Renewable
energy transition and sustainable development: Evidence from China, Energy
Economics, 143: 108232. https://doi.org/10.1016/j.eneco.2025.108232
Renewable energy transition and sustainable
development: Evidence from China
Abstract
Climate change should be proactively dealt with, and the transition towards renewable energy is a
vital approach. However, the socio-economic influence of policy-induced increase in the accommodation
of renewable energy is not yet fully demonstrated. We estimate the green total factor productivity (GTFP)
of 281 Chinese prefectures using the slacks-based measure (SBM) and global Malmquist-Luenberger
(GML) index. Based on this estimation, we find that the increase in renewable energy accommodation
induced by China’s Plan on Clean Energy Accommodation (2018-2020) hinders the green total factor
productivity, mainly technical efficiency. Our findings are confirmed by a series of robustness tests. The
mechanisms in this process are rather complicated. Local governments are supposed to invest in
infrastructures like energy storage systems and inter-regional transmission networks of electricity, which
crowds out their expenditure on science and technology and temporarily and negatively impacts
innovation. However, the adoption of renewable energy also leads to a cleaner energy structure and an
improvement in carbon intensity, which not only implies an improvement in GTFP but also alleviates
climate change. We reckon, therefore, that the effect of renewable energy accommodation might vary
between the long and short term. The analyses on heterogeneity reveal that cities located in southern
China, rely deeply on resource-based industries or with relatively less stock of human capital
experience a greater decrease in GTFP, which adds to the applicability of our findings.
Keywords DEA · Renewable energy · Green total factor productivity · Sustainable
development · Expenditure crowding-out · Energy structure
1 Introduction
Climate change should be proactively dealt with (Hansen et al., 2023). In 2015,
the Paris Agreement was passed, proposing the goal of controlling global warming.
Reduction in carbon emissions is required for alleviating climate change (Duan et al.,
2021). Many climate policies can reduce carbon emissions, including reforestation and
afforestation (Song et al., 2023), application of the rules of circular economy (Nasir et
al., 2017) and carbon capture (Kätelhön et al., 2019). Among these policies, the energy
transition from fossil fuels to renewable energy is a vital one (Kern and Rogge, 2016).
Energy consumption drives economic growth (Long et al., 2015; Shahbaz et al., 2018),
and they have been sharing the trend for decades, as demonstrated in Figure 1.
Fig. 1. Trend in global primary energy consumption and GDP in 1965-2020
Note: The data on global primary energy consumption comes from the BP Statistical Review of
World Energy 2021 (see https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/xlsx/energy-
economics/statistical-review/bp-stats-review-2021-all-data.xlsx). The data of global GDP comes from the
World Bank and are deflated to 2015 USD, (see https://data.worldbank.org/indicator/NY.GDP.MKTP.KD).
However, the carbon emission generated by fossil fuels has also proved to be a
major cause of global ecological degradation (Abbasi et al., 2022). Apart from the air
pollution, health risks and economic loss rooted in the utilization of fossil fuels (e.g.,
Fisher et al., 2021; Kazemiparkouhi et al., 2022; Mohajeri et al., 2023), it will inevitably
be exhausted. For both socio-economic development and environmental sustainability,
the human race needs to promote energy transition (Zhao and You, 2020). The adoption
of renewable energy can promote economic growth while controlling climate change
(Zhang and Chen, 2022a). The transition to renewable energy improves air quality and
reduces carbon emissions and ecological footprints (Zhang and Chen, 2022b; Bashir et
al., 2023). Eventually, it supports the transition to a low-carbon economy (Lin and Zhu,
2019).
Fig. 2. Trend in global consumption of renewable energy
Note: The data on global consumption of renewable energy comes from the BP Statistical Review
of World Energy 2021 (see https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/xlsx/energy-
economics/statistical-review/bp-stats-review-2021-all-data.xlsx).
Renewable energy consists of hydro-power, biomass gasification and biofuels,
geothermal, wind, solar and marine energy (Goldemberg, 2008). The adoption of
renewable energy keeps expanding. As demonstrated in Figure 2, the expansion in the
consumption of hydroelectricity and geothermal, biomass and other is rather stable,
while the rapid growth in the consumption of solar and wind energy is witnessed in this
century. Renewable energy is also an increasingly important supply of electricity (see
Figure 3). Compared with fossil fuels, the utilization of renewable energy generates less
carbon emission (Williams et al., 2012), as well as improvement in economic growth
and green transition (Rehman et al., 2022; Su and Fan, 2022). Therefore, the utilization
of renewable energy leads to sustainable development. China has realized the potential
of renewable energy in the alleviation of climate change. In October 2018, China
launched the Plan on Clean Energy Accommodation (2018-2020) (PCEA)
1
, aiming at
a green-oriented energy transition characterized by a low-carbon energy system and
higher efficiency in the utilization of renewable energy. Nine provinces and
autonomous regions are announced to be the key regions of renewable energy
accommodation. This highlights China’s determination to reduce carbon emissions and
achieve sustainable development, as sustainable development, energy transition and
alleviation of climate change are combined in this policy.
GTFP is a practical measure of sustainability (Liu et al., 2023b), it includes the
factors of resources and environment, making it a better measurement of social welfare
and economic productivity (Tian and Lin, 2017; Cheng et al., 2020). A widely applied
method of estimating GTFP is data envelop analysis (DEA) (e.g., Lee and Lee, 2022;
Chen et al., 2023), which assesses the decision-making units’ (DMU) capability of
maximizing the desirable output and minimizing the undesirable output with given
1
See https://www.ndrc.gov.cn/xxgk/zcfb/ghxwj/201812/t20181204_960958.html.
inputs. The application of DEA requires only the data of inputs and outputs, thus
avoiding the bias caused by the inappropriate setting of the production function.
There is a strong correlation between the environment, energy consumption and
economic growth (Awodumi and Adewuyi, 2020). Energy consumption is an input for
economic growth but also generates the undesirable outputs of carbon emission and air
pollution. By adopting renewable energy, it is possible to reduce the undesirable outputs
of air pollutants and carbon dioxide without affecting energy consumption. Moreover,
the reduction in carbon emissions hinders climate change (Huo et al., 2022), which also
influences GTFP (Song et al., 2022). Therefore, the adoption of renewable energy
might account for changes in GTFP.
We are motivated to investigate how the adoption of renewable energy impacts
GTFP, as research on this theme is rarely seen. We aim to answer two questions on this
topic. First, how does the policy-induced adoption of renewable energy impact GTFP?
We must first correctly estimate the prefectures’ GTFP, which is the foundation of the
analysis. Then, we need to explore the change in GTFP and its decomposition. And we
should further verify the mechanisms through which the PCEA exerts an effect on
GTFP. Second, given the variety of Chinese prefectures, we also clarify the multi-
dimensional heterogeneity among them, including the regional location, reliance on
resource-based industries and stock of human capital, to comprehensively demonstrate
the influence of PCEA.
The estimation of GTFP is conducted using DEA. Specifically, we apply the
combination of slacks-based measure (SBM) and global Malmquist-Luenberger (GML)
productivity index to estimate the GTFP of the 281 prefectures of China. The SBM
model considers slack movement, which allows the comparison of the relative
efficiency of DMUs on the production frontier. Therefore, the SBM model outperforms
radial-based measures (Liu and Wang 2008), especially under the variable returns to
scale (VRS) regime (Zarrin and Brunner, 2023). Meanwhile, the GML index is
established with the global benchmark technology (Oh 2010) while the Malmquist
Luenberger index is not, which adds to the comparability of the technical efficiency of
different DMUs. The GML index measures the change in productivity between the
current term and its previous term. So, the GTFP of each term relative to the base period
equals the product of the current and previous terms’ GML index. Moreover, the
benchmark technology remains unchanged between all terms, which prevents
technology backwards and better demonstrates the change in productivity.
Previously conducted estimations of GTFP generally ignored the input of land
(e.g., Du and Li, 2019; Lee and Lee, 2022; Lyu et al., 2023), which is a vital factor for
economic growth (Zhang et al., 2023; Li et al., 2023b). The input of land is included in
our estimation, accompanied by capital, labor and energy, hence a more comprehensive
and convincing estimation of GTFP. The PCEA classified several provinces as the key
units in renewable energy accommodation, urging them to further improve their
utilization efficiency of wind, solar and hydro-power, which facilitates the application
of the difference-in-differences (DID) method in investigating the impact of the
adoption of renewable energy on GTFP. The nature of the GML index allows us to
decompose the change in GTFP into changes in technical efficiency and technology
progress (Oh, 2010), and the change in return to scale can be measured with the ratio
of constant-return-to-scale (CRS) productivity and variable-return-to-scale (VRS)
productivity, which facilitates more detailed analysis.
We find that, after the PCEA is launched, the growth in GTFP of the key regions
is significantly smaller than that in others, and the gap keeps expanding. The analyses
on the decomposed GML index suggest that the decrease in technical efficiency is
responsible for the drop in GTFP. After several robustness tests, our findings are further
confirmed. In the discussion of mechanism, interestingly, we discover that the effect of
PCEA on GTFP might vary between the long and short term. In the short term, the
infrastructures like energy storage systems and transmission networks demanded by the
PCEA require local governments’ investment, hence crowding out their expenditure on
science and technology (S&T) and hindering innovation activities (Huang et al., 2019;
Wei et al., 2023a), which eventually leads to decrease in GTFP (Zhao et al., 2022b). In
the long term, however, the wider adoption of renewable energy could contribute to the
transition of energy structure. The proportion of electricity in energy consumption
shows a rising trend, and the carbon intensity of energy consumption also significantly
drops, both are pro-productivity changes (Gao et al., 2021). The PCEA exerts a mixed
effect on GTFP. In the short term, cities may suffer from the crowding-out of
government expenditure on S&T. When the infrastructure investment is completed,
however, we believe that the improvement in energy structure and carbon intensity
caused by PCEA can promote GTFP. Furthermore, the analyses on heterogeneity
among prefectures confirm the nature-based north-south difference in China and reveal
that cities relying deeply on the resources-based industry or with relatively less stock
of human capital experience a larger drop in GTFP.
This paper contributes to the existing research in three aspects. First, we achieve a
more accurate and comprehensive estimation of 281 prefectures’ GTFP by including
the input of land in the model of SBM-GML. Based on this estimation, we discover that
the policy-induced energy transition aiming at reducing carbon emissions and
alleviating climate change does not improve GTFP immediately. The large volume of
infrastructure investment required for such expansion crowds out government
expenditure in other fields, S&T in our study. For the developing countries, the liquidity
constraints can limit their utilization of renewable energy. The government should
better balance its expenditure, focusing on the prospect of renewable energy while
alleviating the current negative effect caused by it.
Second, our findings reconfirm the south-north gap in China, but in the angle of
geography. 92 prefectures are directly affected by the PCEA, and 39 of them are located
in southern China. For these cities, the decrease in technology progress is
insignificantly larger. The reason lies in the difference in relief amplitude. The key units
located in southern China are characterized by greater relief amplitude than those
located in northern China and have to spend more on the construction of the
transmission network, causing a larger crowding-out of government expenditure on
S&T.
Finally, our results underline the heterogeneities among cities. Prefectures deeply
relying on the resource-based industry bear the large volume of investment in fixed
assets, which narrows the room for adjusting their fiscal expenditure. Therefore, it is
more difficult for these cities to cope with the crowding-out of expenditure on S&T.
Meanwhile, the lack of human capital, a vital factor of economic development, hinders
the transformation in production. These cities are suffering from a larger decrease in
GTFP, as our results show. The imbalance in development levels remains and is widely
seen, we are therefore convinced that more attention should be paid to the less
developed areas since their welfare also matters.
The remainder of this paper is organized as follows. Section 2 reviews related
literature. Section 3 provides the background of PCEA and the research hypotheses.
Section 4 presents the methodology and data. The empirical results are presented and
discussed in Section 5, accompanied by analyses of mechanisms and heterogeneities.
Finally, Section 6 concludes this paper.
Fig. 3. Trend in the percentage of renewable energy in global electricity generation
Note: The data on global consumption of renewable energy comes from the BP Statistical Review
of World Energy 2021 (see https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/xlsx/energy-
economics/statistical-review/bp-stats-review-2021-all-data.xlsx).
2 Literature review
2.1 Renewable energy and energy transition
2.1.1 Definition of renewable energy
The concept of renewable energy can sometimes be analogous to clean energy and
must be distinguished from the latter. Clean energy includes electricity and natural gas
(Huang and Zou, 2020), which generates no pollution in utilization. However, clean
energy is not guaranteed to be renewable. Nuclear power, for instance, is deemed a
clean source of electricity. However, the nuclear fuel itself is not renewable. It is stated
that renewable energy includes hydro-power, biomass gasification and biofuels,
geothermal, wind, solar and marine energy (Goldemberg, 2008). Since the utilization
of renewable energy does not reduce the volume of material on which it is based, it is
described as “sustainable in perpetuity” (Brundtland, 1987). Renewable energy is now
widely adopted, as we demonstrate in Figures 2 and 3. As a promising substitute for
coal (McElroy et al., 2009), renewable energy is essential in reducing carbon emissions
while prompting economic development. Basically, renewable energy is transformed
into electricity for utilization, which makes it a subset of clean energy.
2.1.2 Influences of the adoption of renewable energy
First, existing literature reveals that the transition to renewable energy has
functioned as a climate policy, effectively reducing greenhouse gas emissions (Tauseef
Hassan et al., 2023), especially carbon dioxide (Rahman and Alam 2021; Khan et al.,
2021). Nagababu et al. (2023) believe that a 240 MW offshore wind farm surrounding
the Indian subcontinent could reduce carbon emissions by 4500 million tons per year
from 2020 to 2050. Yasmeen et al. (2022) point out that the consumption of solar power
also decreases carbon emissions per capita. The reduction in carbon emission is largely
owed to the crowding-out of fossil fuels (Chen et al., 2022), representing energy
transition. The increase in the proportion of renewable energy in both electricity
generation and energy consumption is negatively correlated with global greenhouse gas
emissions (Chien et al., 2023). A higher proportion of renewable-energy-generated
(REG) electricity, accompanied by a wider expansion of electronification, can help
control climate change (Williams et al., 2012). Other than the reduction in carbon
emissions, the transition to renewable energy also significantly facilitates the transition
in other sectors including transportation and construction (Zhang and Chen, 2022b).
Meanwhile, it can also reduce the ecological footprints of manufacturing economies
and improve their sustainability (Bashir et al., 2023). Summing up, the wide adoption
of renewable energy hinders climate change (Brini, 2021; He et al., 2023), and the
energy transition towards renewable energy supports the low-carbon transition of
society and economy (Lin and Zhu, 2019), hence a successful climate policy.
Second, the adoption of renewable energy also influences economic development.
For a long time, GDP and carbon emission have been tightly associated given the
reliance on fossil fuels. However, this association is gradually undermined, as the
consumption of REG electricity can also prompt economic growth (Rehman et al.,
2022). The OECD countries have economically benefited from the consumption of
renewable energy (Inglesi-Lotz, 2016). The mutual promotion between renewable
energy consumption and economic growth also exists in the BRICS countries (Sebri
and Ben-Salha, 2014). The adoption of renewable energy alleviates energy poverty, an
inability to access energy services, by increasing the energy supply and improving
energy efficiency (Zhao et al., 2022a), which leads to economic growth. For developing
countries, especially those with abundant renewable energy resources, the consumption
of such resources positively impacts their economic growth (Gyimah et al., 2022). Chen
et al. (2020) highlight the non-linear nature of such relationships. When the
consumption of renewable energy surpasses a certain threshold level, its prompting
effect on economic growth is more profound (Wang and Wang, 2020). Such a fact
underlines the importance of further investment, which is essential to the optimization
of the deployment of solar and wind power plants and the reduction in the abatement
cost of carbon dioxide (Wang et al., 2023b). Wang et al. (2023c) discover that a
reasonably-proportioned investment in solar power plants and wind power plants
encourages the adoption of renewable energy, which increases both energy supply and
employment. With an increased proportion of renewable energy, the carbon emission
per unit of GDP drops (Meng et al., 2022), which can be interpreted as a hint of
sustainable development characterized by reduced carbon emissions and continuous
economic growth. Plus, fossil fuel is crowded out as the adoption of renewable energy
expands, and the exit of high energy-consuming industries follows (Lee et al., 2023a).
All of these are induced to higher sustainability. Provided with expansion in the
adoption of renewable energy and continuous technical progress, the green transition
of the economy is within sight (Su and Fan 2022).
However, the merit of renewable energy is not shared equally but varies among
countries and regions. Inside China, the project of West-to-East Power Transmission
(xidiandongsong) decreases the proportion of clean energy in the exporting area, while
the energy structure of the importing area is untouched (Huang and Zou, 2020). On a
larger scale, the uncertainty in the relationship between the consumption of non-fossil
fuel and carbon emission is also witnessed (Cang et al., 2021). Inglesi-Lotz (2016)
believes that the consumption of renewable energy prompts economic growth. That,
however, cannot be simply applied to South Asian countries (Rahman and Velayutham,
2020). It is revealed that the environmental and economic effects of the consumption
of renewable energy could be different between regions.
2.1.3 Fluctuation in REG electricity
Renewable energy comes from nature, hence also limited by the change in its
circumstances. The weather is beyond control, which adds to the intermittent problem
of renewable energy, such as the significant fluctuations in voltage, frequency and
volume of the generated electricity. The intermittent problem results in low efficiency
in the utilization of renewable energy (Liu et al., 2023a). Given the grid’s lack of ability
to cope with the violent fluctuation in the volume of electricity, a large amount of wind
power and solar power is left unused in light of the stability of the grid, causing wind
power curtailment and solar power curtailment. For the wider adoption of renewable
energy, such fluctuation must be properly dealt with (Wang et al., 2022). Solutions are
offered. First, an energy storage system with enlarged capacity allows a higher
proportion of REG electricity (Sun et al., 2020), which facilitates the matching between
electricity supply and demand. Second, an inter-regional transmission network can,
considering the unequal distribution of renewable energy resources, enhance the
efficiency of the utilization and alleviate problems including wind power curtailment
(Peng et al., 2021). Therefore, the construction of an energy storage system and
transmission network can expand the adoption of renewable energy, and the investment
to which is indispensable.
2.2 GTFP
Solow (1957) put forward the well-acknowledged definition of total factor
productivity (TFP). The growth in TFP driven by factors including technological
advance accounts for the continuous growth in per-capita income (Krugman, 1994).
However, the neglection of environmental factors causes over-estimation of TFP and
its contribution to economic growth, as well as an overly optimistic idea of the
economic development routine (Cheng et al., 2020). After including the environmental
factors, the estimation of TFP is called GTFP, a better proxy for the efficiency and
sustainability of the economy (Tian and Lin, 2017).
The existing literature has been focusing on the impacting factor of GTFP, which
covers the technical, economic and governmental aspects (Zhang et al., 2021). For
technical factors, advancement in green innovation improves GTFP, as it promotes the
decoupling between economic growth and carbon emission ( Zhang and Da 2015). Such
an effect is more profound in cities that depend less on resource industries (Zhao et al.,
2022b), hinting at the problem of path dependency. For economic factors, resource
misallocation hinders productivity growth (Hsieh and Klenow 2009), and the digital
economy can alleviate this misallocation and improve the industrial structure, which
drives GTFP (Lyu et al., 2023). Meanwhile, the flourishment of the digital economy is
also followed by a reduction in pollution and energy consumption and an improvement
in human capital, which eventually improves GTFP (Ren et al., 2022). Lee and Lee
(2022) find that green finance also positively impacts GTFP, especially in regions with
higher levels of urbanization. For governmental factors, the direct expenditure on S&T
and subsidies offered to enterprises both encourage green innovation (Howell 2017;
Wei et al., 2023) and so do innovation-stimulating policies (Li et al., 2022). Apart from
the mentioned aspects, GTFP is impacted by natural factors including climate change
(Song et al., 2022). The implementation of carbon emission trading as well as the
construction of low-carbon cities, aiming at reducing carbon emissions and hindering
climate change, has exerted a positive effect on productivity (Cheng et al., 2019; Pan
et al., 2022). Meanwhile, the development of circular economy also prompts the total
factor carbon productivity (Cui and Zhang, 2022). By reducing carbon emissions and
alleviating climate change, the adoption of renewable energy improves labor
productivity (Dasgupta et al., 2021).
So far, there has been plenty of research that focuses on the adoption of renewable
energy and GTFP, but the direct relationship between these two objects is relatively
less studied and normally examined across countries and provinces (e.g., Shah et al.,
2023; Sun et al., 2023). Meanwhile, the effect of government policy regarding the
adoption of renewable energy on GTFP should also be further clarified. We are
motivated to investigate the effect of the implementation of PCEA on cities’ GTFP, so
as to contribute to the existing literature.
3 Theoretical background and research hypotheses
3.1 Theoretical background
China has long realized the necessity of couping with climate change. Since the
Twelfth Five-Year Plan (2011-2015), reduction in carbon emissions has been an
important goal in China’s national development strategy. In 2020, China promised to
achieve a carbon peak in 2030, and carbon neutrality in 2060
2
, expressing its
determination to control climate change. During the past years, China has applied
multiple policies to realize this goal. The Chinese government started the construction
of low-carbon cities in 2010
3
, and implemented the energy-saving and emission-
2
See https://www.gov.cn/xinwen/2020-09/22/content_5546168.htm.
3
See https://www.ndrc.gov.cn/xxgk/zcfb/tz/201008/t20100810_964674_ext.html.
reduction pilot policy in 2011
4
, both significantly reducing carbon emissions (Xu et al.,
2022; Zeng et al., 2023). Other than government policy, market means are also widely
adopted. In 2013, China launched the carbon trading pilot policy, which successfully
decreased the total carbon emission and per capita carbon emission in the pilot area (Shi
et al., 2022). Government intervention proves to be an important reason for this
outcome (Lin and Huang, 2022), underlining the coordination of the government and
the market. Meanwhile, development in green finance also leads to lower carbon
intensity of enterprises (Xu et al., 2023), and promotes the decarbonization of the total
economy (Lee et al., 2023b). China pursues decarbonization in all sectors. Fuel cars are
one of the major contributors to carbon emissions (Zahoor et al., 2023), and China
actively promotes the new energy vehicles (NEVs) industry to avoid the carbon
emissions of the transportation sector. With a higher proportion of NEVs, the
transportation-caused carbon emission drops (Zhao and Sun, 2022). Beyond carbon
dioxide, China also pays attention to the reduction of other greenhouse gases. For
example, China has been subsidizing the environmentally friendly disposal of
trifluoromethane (HFC-23) since 2014. By the end of 2019, the total reduction in HFC-
23 emissions reaches 65.3 thousand tons, equalling 966 million tones of carbon dioxide
equivalent emission
5
.
Progress in energy transition also exists. As the largest producer and consumer of
energy in the world
6
, China is devoted to the decarbonization of the energy system and
energy transition. In 2011-2022, the proportion of coal in China’s total energy
consumption decreased from 70.2% to 56.2%, and the proportion of primary electricity
and other energy increased from 8.4% to 17.5%, as displayed in Figure 4. By the end
of June 2023, the installed capacity of renewable energy has exceeded that of coal-fired
power
7
, which is an extraordinary achievement for China, a once coal-reliant country.
However, the adoption of renewable energy is confronted with intermittent problems.
In 2017, the Chinese government launched the Plan on Addressing Wind, Solar and
4
See https://www.ndrc.gov.cn/fggz/tzgg/ggkx/201107/t20110708_1053556.html.
5
See https://www.gov.cn/xinwen/2021-10/27/content_5646697.htm.
6
See http://www.nea.gov.cn/2023-10/27/c_1310747689.htm.
7
See https://www.gov.cn/govweb/lianbo/bumen/202307/content_6895756.htm.
Hydro-power Curtailment (PAWSHC)
8
, emphasizing the problem of renewable
curtailment of several provinces and autonomous regions, including Inner Mongolia,
Yunnan, Sichuan, Gansu, Jilin, Heilongjiang and Xinjiang. Based on the PAWSHC,
the Chinese government further issued the PCEA in October 2018, setting nine
provinces and autonomous regions as key regions in promoting renewable energy
accommodation and demanding them to improve their utilization ratio of renewable
energy. We manually collect the real utilization ratio of the nine key regions and report
them in Table 1, as well as the target ratio raised by the PCEA. We notice that the
targeted utilization ratio of 2018 is higher than that in 2017, implying a tangible effect
in the very year of PCEA’s implementation.
Table 1 The utilization ratio of renewable energy of nine key regions
Type
Area
2017
2018
2019
2020
Wind power
Xinjiang
71%
75%
80%
85%
Gansu
67%
77%
80%
85%
Heilongjiang
86%
90%
92%
94%
Inner Mongolia
85%
88%
90%
92%
Jilin
79%
85%
88%
90%
Hebei
93%
94%
95%
95%
Solar power
Xinjiang
78%
85%
90%
90%
Gansu
80%
90%
90%
90%
Hydro-power
Sichuan
Lower than 90%
90%
92%
95%
Yunnan
Lower than 90%
90%
92%
95%
Guangxi
92%
95%
95%
95%
Note: The data on the utilization ratio of 2017 is manually collected from the National Energy
Administration of China (see https://www.nea.gov.cn/2018-02/01/c_136942234.htm,
http://www.nea.gov.cn/2018-01/24/c_136920159.htm?eqid=f374799e000861f9000000066479cd4e, and
http://www.nea.gov.cn/2018-03/20/c_137051943.htm) and the Development and Reform Commission of
8
See https://www.gov.cn/xinwen/2017-11/14/content_5239536.htm.
Guangxi Zhuang Autonomous Region (see http://fgw.gxzf.gov.cn/fzgggz/dlhd/t2491352.shtml). The targeted
utilization ratio for 2018-2020 comes from the PCEA.
Purposed to promote the utilization ratio of renewable energy, the PCEA proposes
several measures. (1) Planned expansion of the adoption of renewable energy, as
relative investment must be made in accordance with the circumstance of renewable
energy accommodation. (2) Market-oriented reform of electricity transaction, including
the construction of the spot power market and expansion in the inter-provincial
transaction of REG electricity. (3) Institutional innovation covering the quota system
of REG electricity, the priority of renewable energy in power generation and the
formulation of the specific law on renewable energy. (4) Improvement in the
controllability of electricity generation, including the flexibility in the operation of coal-
fired power plants and the predictability of REG electricity. (5) High-quality
infrastructure of the electricity grid, such as the inter-regional transmission network and
energy storage system with enlarged capacity. Clearly, these infrastructures can
effectively address the intermittent problem, empowering the grid to either store or
deliver huge amounts of REG electricity generated in a short time, which echoes
existing literature.
The goal of the PCEA is largely realized. For example, the utilization ratio of wind,
solar and hydropower of Guangxi has all reached 100% in 2018 and 2019
9
. In 2017,
China’s utilization ratio of wind, solar and hydropower was 88%, 94% and 96%,
respectively
10
. By the end of 2022, these indicators reached 96.8%, 98.3% and 98.7%,
respectively
11
. A giant leap is witnessed, and the economic effect of which is in interest.
9
See http://swzx.gxzf.gov.cn/dtxx/tzgg/t3142847.shtml.
10
See http://www.nea.gov.cn/2018-01/24/c_136921015.htm.
11
See http://zfxxgk.nea.gov.cn/2023-09/07/c_1310741874.htm.
Fig. 4. Change in China’s energy structure
Note: This figure illustrates the change in the proportion of coal and primary electricity and others
in China’s total energy consumption, data comes from the National Bureau of Statistics of China
(https://data.stats.gov.cn/).
3.2 Research hypotheses
3.2.1 Expenditure crowding-out
According to Solow (1956), capital is a main input for economic development,
which government expenditure is a part of. As proposed by the PCEA, the construction
of an inter-regional transmission network and energy storage system requires
investments (Zhuo et al., 2022), which largely rely on government expenditure (IEA,
2023). However, the increased investment in these infrastructure constructions could
crowd out expenditure on other affairs, such as S&T. Given the strong correlation
between government expenditure on S&T and innovation activities (e.g., Howell, 2017;
Huang et al., 2019), the decrease in expenditure on S&T may provide less support to
innovative activities and hinders GTFP (Zhao et al., 2022b). Therefore, we put forward
our first hypothesis:
H1: The implementation of PCEA crowds out government expenditure on S&T
and hinders innovation, which decreases GTFP.
3.2.2 Energy structure
REG electricity prompts economic growth like fossil fuel does (Rehman et al.,
2022), making it a competent input. Meanwhile, the increased proportion of
decarbonized electricity in the energy supply decreases the carbon intensity of energy
consumption and economic growth, which is induced to the reduction in carbon
emission (Kaya, 1989). Summing up, the adoption of renewable energy creates a
cleaner energy structure. On one hand, it provides an adequate energy supply, which is
vital for the growth in the desirable output of GDP. On the other hand, it generates less
carbon emission, which helps address the problem of climate change and raises
productivity (Dasgupta et al., 2021). Therefore, we put forward our second hypothesis:
H2: The implementation of PCEA creates a cleaner energy structure and decreases
the carbon intensity of energy consumption, which increases GTFP.
4 Methodology and data
4.1 Methodology
4.1.1 Estimation of GTFP
Tone (2003) put forward the SBM model containing undesirable outputs. The
SBM model is capable of assessing the impact of non-zero input and output relaxation
and comprehensively measuring GTFP (Fang et al., 2021), and is applied in this paper
to estimate GTFP. Let there be T terms and N DMUs
that utilize M inputs
to generate Q desirable outputs
and H undesirable outputs
. And the
production possibility set (P) at the t term is defined as follows:
Oh (2010) defines the global production possibility set as:
and the SBM model of DMU n under it is presented as follows:
where λn is the indicator weight, and ρ denotes the efficiency of DMU n. Oh (2010)
proposes the following GML index, which allows the comparison with a global
benchmark technology.
is the efficiency estimated by SBM with a global benchmark technology.
denotes the change in GTFP from term to term . being
larger than 1 means an increase in GTFP. Therefore, the GTFP is acquired from the
cumulative multiplication of the GML index, which represents the change in GTFP
relative to the base period, 2002 in this paper. We further decompose the GML index
into changes in technical efficiency and technology progress (Emrouznejad and Yang,
2016). The GTFP estimated with equations (1) to (3) is based on the VRS setting, we
should also consider the change in return to scale. According to Cooper et al. (2006),
the scale effect can be measured by the ratio of CRS productivity and VRS productivity,
and we apply this strategy in our analysis.
We must note that previous estimations of regional GTFP mainly include the input
of capital, labor and energy (e.g., Du and Li, 2019; Lee and Lee, 2022; Lyu et al., 2023)
but tend to ignore the input of land if not on the topic of agricultural productivity.
However, the input of land is indispensable for economic development (Zhang et al.,
2023; Li et al., 2023b), which we include in our estimation. The extent of change caused
by this adjustment is rather minor, but it implies the necessity of re-evaluating the
regional GTFP that has been estimated before, which covers a wide range of research.
Therefore, we have, according to Bergh et al. (2022), made an incremental
methodological contribution to the application of DEA.
4.1.2 Difference-in-differences
We estimate the impact of PCEA on cities’ GTFP with the following equation:
where i denotes the city, and t denotes the year. represents the GTFP and other
productivity indicators of the city i in year t. is a dummy variable, equaling
one for cities belonging to the key regions (the treated group) and equaling zero
otherwise (the control group). denotes when the PCEA was implemented. As
explained in section 3.1, the PCEA was launched in October 2018, but it has exerted an
effect on renewable energy accommodation in that very year. Therefore, for the year
2018 and later, equals one. is a vector of controls for the city-level
characteristics. and are the city-fixed effects and year-fixed effects.
represents the disturbance term. For the first time, we investigate the effect of the
policy-induced adoption of renewable energy following the implementation of PCEA
on regional GTFP by combining DEA and DID, which is of non-negligible practical
importance.
4.2 Data and variables
First, the estimation of GTFP is conducted. The SBM model requires data on
inputs, desirable outputs and undesirable outputs. Referring to Lyu et al. (2023) and
Song et al. (2020), the input includes capital, labor, energy and land, the desirable
output is GDP, and the undesirable outputs contain air pollution and carbon emission.
Since Solow (1956), the input of capital and labor has been deemed a vital factor of
economic growth. Meanwhile, energy consumption, especially fossil fuels, drives both
economic growth and carbon emissions (Sebri and Ben-Salha 2014; Long et al., 2015).
The input of land, as we demonstrate above, is dispensible for economic development
(Zhang et al., 2023; Li et al., 2023b). For the output variables, GDP remains a major
measure of economic development, while air pollution is tightly associated with energy
consumption (Kazemiparkouhi et al., 2022). And the change in carbon emission, to a
large extent, reflects the effectiveness of climate policy. The input of capital is
measured by capital stock calculated with the perpetual inventory method (Reinsdorf,
2005), the input of labor refers to total employment in urban units, energy consumption
refers to electricity consumption, and the input of land equals the city’s coverage of the
built-up area. Air pollution is represented by the annual concentration of PM2.5 and the
data comes from Wei and Li (2019). Finally, the data on carbon emission is collected
from the Emissions Database for Global Atmospheric Research (EDGAR)
12
.
Meanwhile, we also control for various characteristics of the cities, including (1)
economic growth, the annual growth of real GDP; (2) industrial structure, the Theil
index represents the rationalization of industrial structure:
12
See https://edgar.jrc.ec.europa.eu/.
where i denotes industry, Yi and Li represent the output and employees of the primary,
secondary and tertiary industry, respectively; (3) openness, the city’s foreign direct
investment (FDI); (4) industrial development, the number of industrial enterprises
above designated size; (5) internet, the number of users of international internet; (6)
medical services, beds of hospital per ten thousand people; (7) urbanization, the
proportion of urban resident in total resident; (8) urban vegetation, the green coverage
of built-up areas. Relative data is collected from the China City Statistical Yearbook
and covers the period of 2012-2022. All nominal variables are deflated by CPI, and we
take the natural logarithm except economic growth, urbanization and urban vegetation.
The summary statistics of the main variables, namely the GTFP and its decomposition
and the control variables, are reported in Table 3. Notably, during the period of 2012-
2022, only the scale effect increases, while the GTFP, technical efficiency and
technology progress all drops.
Table 2 Summary statistics of main variables
Variable
Units
Obs
Mean
SE
Min
Max
GTFP
/
3,091
0.982
0.0304
0.900
1.070
Technical efficiency
/
3,091
0.999
0.0299
0.925
1.100
Technology progress
/
3,091
0.984
0.0367
0.897
1.080
Scale effect
/
3,091
1.019
0.0201
0.970
1.095
Economic growth
%
3,102
5.612
7.010
-23.41
23.15
Industrial structure
/
2,754
0.280
0.225
-0.261
0.967
Openness
/
2,897
11.58
1.907
6.439
15.58
Industrial
development
/
3,102
6.633
1.079
4.060
9.080
Internet
/
3,101
14.49
2.082
11.51
20.18
Medical services
/
3,102
485.6
171.2
210.0
1,019
Urbanization
%
2,997
57.29
14.28
29.27
94.87
Urban vegetation
%
3,102
6,488
8,382
654
50,734
5 Empirical results
5.1 Baseline regression
PCEA’s effect on GTFP is estimated with equation (5), and the results are
presented in Table 3. Aspiring for robust estimation, we gradually include the control
variables in the regression. As the control variables increase, the coefficient in interest,
, remains significant and negative. According to the results in column (4), the
implementation of PCEA causes the cities’ GTFP to decrease by, on average, 0.0036
(0.37% of the mean value). This estimation is rather counter-intuitive, given the
literature claiming the positive effect of renewable energy accommodation on economic
growth. We further decompose the GTFP into technical efficiency and technology
progress and consider the change in scale effect, as the effect on them is displayed in
Table 4. The technical efficiency decreases by 0.0047 (0.47% of the mean value), and
the technology progress and scale effect remain untouched. Therefore, the drop in
GTFP is mainly driven by the hindered growth in technical efficiency.
Table 3 Baseline results: GTFP
GTFP
(1)
(2)
(3)
(4)
PCEA×Post
-0.0049***
-0.0035*
-0.0038*
-0.0036*
(0.0018)
(0.0019)
(0.0019)
(0.0018)
Economic growth
0.0002***
0.0002***
0.0003***
(0.0000)
(0.0000)
(0.0000)
Industrial structure
-0.0054
-0.0057
-0.0051
(0.0037)
(0.0038)
(0.0039)
Openness
-0.0000
-0.0001
-0.0001
(0.0005)
(0.0004)
(0.0004)
Industrial
development
0.0022
0.0025
0.0020
(0.0022)
(0.0022)
(0.0022)
Internet
0.0005
0.0009
(0.0006)
(0.0006)
Medical services
0.0000***
0.0000**
(0.0000)
(0.0000)
Urbanization
-0.0074
(0.0108)
Urban vegetation
0.0000***
(0.0000)
City FE
YES
YES
YES
YES
Year FE
YES
YES
YES
YES
Observations
3,091
2,625
2,625
2,579
Adj. R-squared
0.850
0.865
0.866
0.872
Note: Robust standard errors clustered at the city level are reported in parentheses. *p<0.1; **p<0.05;
***p<0.01, so are the following tables.
Table 4 Baseline results: technical efficiency, technology progress and scale effect
Technical
efficiency
Technology
progress
Scale effect
(1)
(2)
(3)
PCEA×Post
-0.0047*
0.0009
-0.0004
(0.0026)
(0.0021)
(0.0008)
Controls
YES
YES
YES
City FE
YES
YES
YES
Year FE
YES
YES
YES
Observations
2,579
2,579
2,579
Adj. R-squared
0.717
0.858
0.937
Note: Robust standard errors clustered at the city level are reported in parentheses.
5.2 Robustness tests
5.2.1 Parallel trend assumption
The indispensable precondition of a reliable DID estimation is the existence of a
parallel trend. We apply the event study approach to confirm whether a similar trend in
productivity is shared between the prefectures before PCEA comes into effect, and the
equation is presented as follows:
where captures the inter-group difference in productivity in the year t, and the other
variables and parameters are consistent with equation (5). The results are displayed in
Figure 5. Clearly, before the implementation of PCEA in 2018, there was no significant
inter-group difference in the productivity indicators. Therefore, the parallel trend
assumption is satisfied. Moreover, the GTFP and technical efficiency of the key regions
are lower than that of the non-key regions after the implementation of PCEA, echoing
our baseline results.
Fig. 5. Results of the event study approach
Note: This figure presents the coefficients and 99% confidence intervals of GTFP, technical
efficiency, technology progress and scale efficiency, year by year. It shows no existence of difference in
the trend of these indicators between the key and non-key units, confirming the parallel trend.
5.2.2 Measuring energy consumption with nighttime light
In our estimation of GTFP, the energy consumption is proxied by the prefectures’
total electricity consumption. However, electrification is far from being completed, and
the fossil fuel is still widely applied in sectors such as transportation. Therefore, we
replace electricity consumption with nighttime lights. Nighttime lights are largely owed
to human behavior, making them a good indicator of energy consumption (Yagi et al.,
2010). We obtain data on stable nighttime lights from the National Oceanic and
Atmospheric Administration (NOAA). Specifically, the data for 2012-2013 comes
from the DMSP-OLS, and the data for 2014-2022 comes from the VIIRS-DNB. They
are reasonably merged and applied for the estimation of GTFP following Chen et al.
(2019). Similarly, the GTFP is decomposed, with which the equation (5) is again
estimated. The results presented in Table 5 suggest that the change in the input variable
does not undermine our baseline results. The light-based GTFP decreases by, at
average, 0.0033, which is consistent with baseline results. The technical efficiency
drops by, at average, -0.0054, while the technology progress and scale effect remain
unchanged.
Table 5 Measuring energy consumption with night light
GTFP
Technical
efficiency
Technology
progress
Scale effect
(1)
(2)
(3)
(4)
PCEA×Post
-0.0033***
-0.0054***
0.0025
0.0005
(0.0011)
(0.0019)
(0.0015)
(0.0005)
Controls
YES
YES
YES
YES
City FE
YES
YES
YES
YES
Year FE
YES
YES
YES
YES
Observations
2,557
2,557
2,557
2,557
Adj. R-
squared
0.926
0.721
0.848
0.972
Note: Robust standard errors clustered at the city level are reported in parentheses.
5.2.3 Propensity score matching
The key region of PCEA is not randomly chosen but decided by the central
government. Therefore, the probability of being affected by the PCEA varies among
cities, which might cause selection bias and undermine the reliability of our baseline
regression. We turn to the propensity score matching to seek a more balanced sample.
Precisely, we apply the calliper matching with a radius of 0.01. All control variables
are used as matching variables, the balance of which after matching is illustrated in
Figure A1. We then estimate equation (5) with the matched sample. According to the
results rendered in Table 6, the baseline results are reliable.
Table 6 PSM-DID
GTFP
Technical
efficiency
Technology
progress
Scale effect
(1)
(2)
(3)
(4)
PCEA×Post
-0.0035*
-0.0048*
0.0012
-0.0002
(0.0019)
(0.0027)
(0.0022)
(0.0008)
Controls
YES
YES
YES
YES
City FE
YES
YES
YES
YES
Year FE
YES
YES
YES
YES
Observations
2,556
2,556
2,556
2,556
Adj. R-squared
0.872
0.717
0.857
0.936
Note: Robust standard errors clustered at the city level are reported in parentheses.
5.2.4 Shortened period
Apart from the accommodation of renewable energy, environmental regulation
could also influence air pollution and carbon emission, therefore exerting an effect on
GTFP and interfering with our baseline results. In September 2013, China released the
Air Pollution Prevention and Control Action Plan (APPCAP)
13
, which was formed with
unprecedented stringency (Li et al., 2023a). The APPCAP has significantly improved
air quality and generated huge health benefits (Huang et al., 2018), both of which
impact GTFP. Aiming at improvement in air quality, the APPCAP demands a
transformation in energy structure characterized by wider adoption of renewable energy
(Sheehan et al., 2014), which overlaps with the PCEA. To exclude its impact on our
estimation, we shortened the period to 2014-2022, and the corresponding results are
presented in Table 7, which are consistent with the baseline results.
Table 7 Shortened period: 2014-2022
GTFP
Technical
efficiency
Technology
progress
Scale effect
(1)
(2)
(3)
(4)
PCEA×Post
-0.0037**
-0.0048*
0.0008
-0.0001
(0.0019)
(0.0025)
(0.0020)
(0.0007)
Controls
YES
YES
YES
YES
City FE
YES
YES
YES
YES
Year FE
YES
YES
YES
YES
Observations
2,027
2,027
2,027
2,027
Adj. R-squared
0.890
0.731
0.870
0.943
Note: Robust standard errors clustered at the city level are reported in parentheses.
13
See https://www.gov.cn/jrzg/2013-09/12/content_2486918.htm.
5.2.5 Exclude special cities
Our sample covers 281 prefectures in China, which are not homogenous at the
administrative level. These prefectures can be classified into municipalities directly
under the central government (MDUCGs), separately planned cities (SPCs), provincial
capital cities and normal prefectures. The former three types of cities are usually
deemed as higher administratively ranked. Cities with higher administrative levels are
generally characterized by better economic performance (Luo et al., 2023; Wang et al.,
2023a).To rule out their intervention with our baseline results, we exclude all
MDUCGs, SPCs and provincial capital cities, and regress equation (5) with the
remaining normal cities. The results are presented in Table 8. We notice that the
coefficients are consistent with the baseline result, implying the reliability of the latter.
Table 8 Regression with normal cities
GTFP
Technical
efficiency
Technology
progress
Scale effect
(1)
(2)
(3)
(4)
PCEA×Post
-0.0033*
-0.0049*
0.0015
-0.0005
(0.0019)
(0.0027)
(0.0022)
(0.0008)
Controls
YES
YES
YES
YES
City FE
YES
YES
YES
YES
Year FE
YES
YES
YES
YES
Observations
2,396
2,396
2,396
2,396
Adj. R-squared
0.854
0.720
0.846
0.939
Note: Robust standard errors clustered at the province level are reported in parentheses.
5.2.6 Placebo test
We have excluded the potential interference of the APPCAP, however, China has
launched myriad policies regarding economic growth and social development. One
might wonder whether other random factors disturb the impact of PCEA on GTFP. To
cope with this concern, we conduct the placebo test. Specifically, we randomly sample
the treated group and estimate equation (5) with it, recording the false coefficient
subsequently. This process is repeated 500 times, and the distribution of the false
coefficients is demonstrated in Figure 6. It shows that the mean value of false
coefficients equals zero and differs from our baseline results which is marked by the
dashed line. We are therefore convinced that our results are not disturbed by other
policies.
Fig. 6. Results of the placebo test
Note: This figure illustrates the distribution of coefficients estimated with randomly sampled key
units, and the mean of which equals zero, largely differs from the baseline results marked by the dashed
lines.
5.3 Mechanisms
5.3.1 Expenditure crowding-out
Government policy and expenditure matter for development in many fields,
including innovation and infrastructure construction (Howell, 2017; IEA, 2023).
Demanded with the construction of an inter-regional transmission network and energy
storage system, local government would have to cut their expenditure on other affairs
to meet this need, hence the crowding-out of expenditure on S&T. Our analyses on
government expenditure are presented in Table 9. We notice that the total expenditure
of local governments is not impacted by the PCEA, while their expenditure on S&T
significantly drops by 40.53%, which is enormous. Subsequently, the proportion of
expenditure on S&T in the total expenditure decreases by 0.5177%.
Table 9 Mechanism: government expenditure
Total expenditure
Expenditure on S&T
The proportion of
expenditure on S&T
in the total
expenditure
(1)
(2)
(3)
PCEA×Post
-0.0135
-0.4053***
-0.5177***
(0.0162)
(0.0677)
(0.0884)
Controls
YES
YES
YES
City FE
YES
YES
YES
Year FE
YES
YES
YES
Observations
2,589
2,588
2,589
Adj. R-squared
0.980
0.922
0.851
Note: Robust standard errors clustered at the city level are reported in parentheses.
This change is reflected in the application of patents, the indicator of local
innovation activities. According to Table 10, the patent application decreases, on
average, by 12.04% after the implementation of PCEA, and the application for
invention patent drops by 30.67% on average. The invention patent requires more input
than the utility model patent (Huang et al., 2021) and, therefore is more sensitive to the
crowding-out of government expenditure on S&T. However, the negative effect of
expenditure crowding out tends to gradually vanish as the investment is completed, we
therefore believe that this effect only exists in the short term.
Table 10 Mechanism: innovation activities
Total patent
application
Application for
invention patent
Application for
utility model patent
(1)
(2)
(3)
PCEA×Post
-0.1204**
-0.3067***
0.0563
(0.0574)
(0.0804)
(0.0477)
Controls
YES
YES
YES
City FE
YES
YES
YES
Year FE
YES
YES
YES
Observations
2,589
2,589
2,589
Adj. R-squared
0.968
0.951
0.966
Note: The data on applications for patents comes from the China National Intellectual Property
Administration. Robust standard errors clustered at the city level are reported in parentheses.
5.3.2 Energy structure
As previously explained, REG electricity can promote economic growth just like
fossil fuels do. Meanwhile, the widened adoption of decarbonized electricity
contributes to the reduction of carbon emissions, to which the GTFP also benefits. Due
to the problem of data availability, it is hard to distinguish the consumption of REG
electricity from fossil-fuel-generated electricity at the city level. However, given the
increasing proportion of renewable energy in total electricity generation
14
, the increase
in REG electricity consumption is, to some extent, reflected by the change in total
electricity consumption. According to column (1) of Table 11, the total energy
consumption increase by, on average, 11.42%, underlining that accommodation of
renewable energy adds to the energy supply to the economy. We further look into the
change in energy structure proxied by the proportion of electricity consumption in total
14
See http://zfxxgk.nea.gov.cn/2023-09/07/c_1310741874.htm.
energy consumption. The coefficient in column (2) of Table 11 suggests that this ratio
increases, though not significant, by about 0.63%. It should be interpreted as a trend in
the evolution of energy structure.
Moreover, the change in energy structure leads to an improvement in carbon
intensity of energy consumption. We investigate the PCEA’s effect on the carbon
emission per unit of total energy consumption, and the results are presented in column
(3) of Table 11. It is indicated that after the implementation of PCEA, the carbon
emission generated by the energy consumption of 10000 tons of standard coal
decreases, on average, by 23.0734 tons. The drop in the carbon intensity of energy
consumption implies PCEA’s effectiveness in reducing carbon emissions. With less
undesirable output under the given input, the GTFP can be improved.
Table 11 Mechanism: energy structure
Total energy
consumption
Proportion of
electricity in total
energy
consumption
Carbon emission
per unit of energy
consumption
(1)
(2)
(3)
PCEA×Post
0.1142*
0.6260
-23.0734**
(0.0675)
(1.2086)
(10.2630)
Controls
YES
YES
YES
City FE
YES
YES
YES
Year FE
YES
YES
YES
Observations
2,197
2,197
2,189
Adj. R-squared
0.905
0.754
0.647
Note: The data on total energy consumption comes from the Bureau of Statistics of provinces and
prefectures and is only available until 2019. The unit of electricity consumption has been converted to
10000 tons of standard coal, which is consistent with total energy consumption. Robust standard errors
clustered at the city level are reported in parentheses.
5.4 Analyses of heterogeneity
5.4.1 Regional heterogeneity
The regional gap is widely seen. In China, compared with the east-west gap, the
expanding gap between the north and south deserves more attention (Zhou, 2000).
Following Zhou (2000), we separate our sample into the northern part and southern part
to estimate this regional heterogeneity, and the results are presented in Table 12. The
dummy variable South equals one for cities located in southern China.
Unconventionally, the more developed southern region experiences a significantly
larger drop in technology progress. It does not deny the existence of an economic gap
between the north and south. Instead, the reason lies in, we reckon, the difference in
geographical condition.
Landforms vary across China, as the average relief amplitude differs among
regions. Three of the key regions (Sichuan, Yunan and Guangxi) are located in southern
China, and the other six are located in the north. The average relief amplitude of China
is 1.17 (You et al., 2018), while that of the northern key regions is 1.187, and 2.267 for
the southern key regions. Therefore, the latter could be encountered with higher costs
in the construction of the inter-regional transmission network, hence a larger crowding-
out of expenditure on S&T (see Table A1).
Table 12 Heterogeneity: regional location
GTFP
Technical
efficiency
Technology
progress
Scale effect
(1)
(2)
(3)
(4)
PCEA×Post×South
-0.0040
0.0040
-0.0072*
0.0021
(0.0030)
(0.0046)
(0.0038)
(0.0014)
PCEA×Post
-0.0017
-0.0067**
0.0044*
-0.0014
(0.0019)
(0.0034)
(0.0025)
(0.0010)
Controls
YES
YES
YES
YES
City FE
YES
YES
YES
YES
Year FE
YES
YES
YES
YES
Observations
2,579
2,579
2,579
2,579
Adj. R-squared
0.872
0.717
0.858
0.937
Note: South is the dummy variable denoting whether the city is located in southern China. Robust
standard errors clustered at the city level are reported in parentheses.
5.4.2 Mining industry reliance heterogeneity
Broadly viewed, industrial development relies on resource endowment. Naturally,
cities enjoying ample resource endowment tend to form an industrial structure with
resources-based industries at the centre. The reliance on resource industries crowds out
investment in human capital accumulation and technology and exaggerates
environmental degradation, which hinders GTFP (Cheng et al., 2020; Shi et al., 2024).
The wide-observed positive effect of green innovation on GTFP is also less profound
in cities deeply relying on resource industries (Zhao et al., 2022b). Furthermore, for
cities relying on coal mining and processing, the adoption of renewable energy might
be confronted with greater obstacles.
In November 2013, the Chinese government claimed 262 cities to be resource-
based cities
15
, including 126 prefectures. These cities deeply rely on resource-based
industries including mining and processing of minerals, facing severe environmental
problems and the urgent need for economic transformation. We construct the dummy
variable Res that equals one for resource-based cities, and the results are presented in
Table 13. It shows that the technology progress in resource-based cities is significantly
slower than in other cities, implying that the reliance on resource-based industry hinders
their green transition and sustainable development. Meanwhile, the resource-based
cities do experience a larger increase in scale effect, which demonstrates an
improvement in the return to scale in their energy-intensive sectors (Yang, 2019).
Table 13 Heterogeneity: industry dependence
15
See https://www.gov.cn/gongbao/content/2013/content_2547140.htm.
GTFP
Technical
efficiency
Technology
progress
Scale effect
(1)
(2)
(3)
(4)
PCEA×Post×Res
-0.0035
0.0032
-0.0066*
0.0023*
(0.0031)
(0.0046)
(0.0036)
(0.0013)
PCEA×Post
-0.0018
-0.0064*
0.0043
-0.0015
(0.0016)
(0.0033)
(0.0029)
(0.0009)
Controls
YES
YES
YES
YES
City FE
YES
YES
YES
YES
Year FE
YES
YES
YES
YES
Observations
2,579
2,579
2,579
2,579
Adj. R-squared
0.872
0.717
0.858
0.937
Note: Res is the dummy variable denoting whether the city is resource-based. Robust standard errors
clustered at the city level are reported in parentheses.
5.4.3 Human capital stock heterogeneity
Human capital is an increasingly important input for economic development
(Wang et al., 2021). An abundant supply of human capital not only drives industrial
upgrades (Ni et al., 2023) but also prompts green innovation (Asiaei et al., 2023).
Colleges and universities are important providers of human capital (Kong et al., 2022),
and they themselves are also fountains of innovation (Laursen and Salter, 2006; Chen
et al., 2011). In 1995, China launched the Project 211, aiming at fostering leading
universities. This project contains 112 universities, representing the outstanding higher
education resources of China. We construct the dummy variable of Hcap, for cities
owning at least one Project 211 university, Hcap equals one. The results are presented
in Table 14, we notice that in cities with outstanding higher education resources, the
drop in GTFP is significantly smaller. This result emphasizes the role of research
institutions and human capital in achieving sustainable development and green
transformation.
Table 14 Heterogeneity: human capital
GTFP
Technical
efficiency
Technology
progress
Scale effect
(1)
(2)
(3)
(4)
PCEA×Post×Hcap
0.0058**
-0.0009
0.0073
0.0016
(0.0025)
(0.0055)
(0.0052)
(0.0010)
PCEA×Post
-0.0043**
-0.0046
0.0001
-0.0005
(0.0020)
(0.0029)
(0.0022)
(0.0009)
Controls
YES
YES
YES
YES
City FE
YES
YES
YES
YES
Year FE
YES
YES
YES
YES
Observations
2,579
2,579
2,579
2,579
Adj. R-squared
0.872
0.717
0.858
0.937
Note: Edu is the dummy variable denoting whether the city owns at least one Project 211 university.
Robust standard errors clustered at the city level are reported in parentheses.
6 Conclusion
Whether the adoption of renewable energy can reduce carbon emissions while
maintaining economic growth is vital to the green transition of the economy and
sustainable development. We assess the effect of PCEA, a sustainability-targeted place-
based policy stimulating the adoption of renewable energy, on GTFP with the DID
method. The results show that in the first few years after its implementation, the GTFP
decreases. In the analyses on mechanisms, we discover that the PCEA causes the
crowding-out of government expenditure on S&T and creates a cleaner energy structure
simultaneously. The former suppresses innovation activities, while the latter could
improve GTFP. In the short term, the crowding-out effect is dominant, hence the drop
in GTFP. However, when the investment in infrastructure is completed, an enhanced
energy structure accompanied by an improved carbon intensity of energy consumption
and economic growth should be prompting GTFP. Therefore, the energy transition and
the alleviation of climate change would eventually be combined.
6.1 Theoretical implications
We, for the first time, investigate the effect of policy-induced adoption of
renewable energy driven by the implementation of PCEA on prefectures’ GTFP using
the DID method. Furthermore, we put forward the hypotheses in perspectives of factor
supply (including capital and energy) and carbon intensity, which accounts for the
varying effect of PCEA in the short and long term and hinders that the energy transition
comes with a temporary price. These results can not be achieved without the application
of DEA. As a non-parametric method, DEA does not require an ex-ante setting of
production function, which avoids the interfere of inappropriate function form.
Moreover, we include the land input, which empowers us with a more comprehensive
and accurate estimation of GTFP. Admittedly, the DEA method lays a solid foundation
for our research, without which our whole analysis would hardly be completed.
6.2 Practical implications
Our findings provide several policy implications for promoting the adoption of
renewable energy while avoiding economic loss. First, our analyses show that the
negative effect of PCEA is rooted in its requirement for infrastructure investment and
the subsequent crowding-out of government expenditure on S&T. Therefore, the central
government should provide more financial support to local government, especially
those in less-developed regions with severe natural conditions. Besides, it is
recommended to include private capital in the construction and operation of energy
storage systems, which reduces the financial pressure on the government and improves
efficiency. Second, we prove that the implementation of PCEA creates a cleaner energy
structure and improves the carbon intensity of energy consumption and economic
growth, suggesting the necessity of electrification. For this, we should further improve
the controllability and predictability of REG electricity. On the other hand, the complete
exit of firepower is unwise. Instead, we need to add more flexibility and sustainability
to it, making it a sufficient supplement to renewable energy. Third, our results reveal
the structural problem in cities relying on resource-based industries. Their distorted
industrial structure significantly hinders the green transition, implying an alarming
prospect after the exhaustion of resources reserve. More policy and financial support
are needed for them to build a diversified industrial system, which empowers them with
stronger resistance to risks and shocks as well as the capability to further develop even
after the depletion of resources. Finally, the inter-city flow of factors including
technology and human capital should be prompted, especially from cities with
outstanding higher education resources. Furthermore, the expansion in metropolitan
areas and regional integration can facilitate the factor flow and operation on a larger
scale, both spatially and quantitively. In this process, the collaborative development of
cities is gradually achieved, which is in correspondence with the spirit of common
prosperity.
6.3 Limitations and future scope
We must admit that this paper is largely limited by the problem of data availability.
The data applied in this paper only covers up to 2022, due to which our conjecture about
the long-term effect of PCEA is not verified yet. We are looking forward to a further
investigation on this topic in the coming years, for more data will be available by then.
For future research, we believe that more attention should be paid to the counties,
enterprises and even individuals, to obtain a more sophisticated analysis. Meanwhile,
more diversified mechanisms through which the adoption of renewable energy impacts
GTFP should be explored, in which valuable practical implications lie.
References
Abbasi, K.R., Shahbaz, M., Zhang, J., Irfan, M., Alvarado, R., 2022. Analyze the
environmental sustainability factors of China: The role of fossil fuel energy and
renewable energy. Renew. Energy 187, 390–402.
https://doi.org/10.1016/j.renene.2022.01.066
Asiaei, K., O’Connor, N.G., Barani, O., Joshi, M., 2023. Green intellectual capital and
ambidextrous green innovation: The impact on environmental performance.
Bus. Strategy Environ. 32, 369–386. https://doi.org/10.1002/bse.3136
Awodumi, O.B., Adewuyi, A.O., 2020. The role of non-renewable energy consumption
in economic growth and carbon emission: Evidence from oil producing
economies in Africa. Energy Strategy Rev. 27, 100434.
https://doi.org/10.1016/j.esr.2019.100434
Bashir, M.F., Pan, Y., Shahbaz, M., Ghosh, S., 2023. How energy transition and
environmental innovation ensure environmental sustainability? Contextual
evidence from Top-10 manufacturing countries. Renew. Energy 204, 697–709.
https://doi.org/10.1016/j.renene.2023.01.049
Bergh, D.D., Boyd, B.K., Byron, K., Gove, S., Ketchen, D.J., 2022. What constitutes a
methodological contribution? J. Manag. 48, 1835–1848.
https://doi.org/10.1177/01492063221088235
Brini, R., 2021. Renewable and non-renewable electricity consumption, economic
growth and climate change: Evidence from a panel of selected African
countries. Energy 223, 120064. https://doi.org/10.1016/j.energy.2021.120064
Brundtland, G.H., 1987. Our Common Future: Report of the World Commission on
Environment and Development. World Commission on Environment and
Development, Geneva.
Cang, D., Chen, G., Chen, Q., Sui, L., Cui, C., 2021. Does new energy consumption
conducive to controlling fossil energy consumption and carbon emissions?-
Evidence from China. Resour. Policy 74, 102427.
https://doi.org/10.1016/j.resourpol.2021.102427
Chen, C., Pinar, M., Stengos, T., 2020. Renewable energy consumption and economic
growth nexus: Evidence from a threshold model. Energy Policy 139, 111295.
https://doi.org/10.1016/j.enpol.2020.111295
Chen, D., Zhang, Y., Yao, Y., Hong, Y., Guan, Q., Tu, W., 2019. Exploring the spatial
differentiation of urbanization on two sides of the Hu Huanyong Line -- based
on nighttime light data and cellular automata. Appl. Geogr. 112, 102081.
https://doi.org/10.1016/j.apgeog.2019.102081
Chen, J., Chen, Y., Vanhaverbeke, W., 2011. The influence of scope, depth, and
orientation of external technology sources on the innovative performance of
Chinese firms. Technovation 31, 362–373.
https://doi.org/10.1016/j.technovation.2011.03.002
Chen, X., Chen, Y., Huang, W., Zhang, X., 2023. A new Malmquist-type green total
factor productivity measure: An application to China. Energy Econ. 117,
106408. https://doi.org/10.1016/j.eneco.2022.106408
Chen, Y., Shao, S., Fan, M., Tian, Z., Yang, L., 2022. One man’s loss is another’s gain:
Does clean energy development reduce CO2 emissions in China? Evidence
based on the spatial Durbin model. Energy Econ. 107, 105852.
https://doi.org/10.1016/j.eneco.2022.105852
Cheng, J., Yi, J., Dai, S., Xiong, Y., 2019. Can low-carbon city construction facilitate
green growth? Evidence from China’s pilot low-carbon city initiative. J. Clean.
Prod. 231, 1158–1170. https://doi.org/10.1016/j.jclepro.2019.05.327
Cheng, Z., Li, L., Liu, J., 2020. Natural resource abundance, resource industry
dependence and economic green growth in China. Resour. Policy 68, 101734.
https://doi.org/10.1016/j.resourpol.2020.101734
Chien, F., Chau, K.Y., Sadiq, M., 2023. Impact of climate mitigation technology and
natural resource management on climate change in China. Resour. Policy 81,
103367. https://doi.org/10.1016/j.resourpol.2023.103367
Cooper, W.W., Seiford, L.M., Tone, K., 2006. Data Envelopment Analysis: A
Comprehensive Text with Models, Applications, References and DEA-Solver
Software. Springer, New York.
Cui, T., Zhang, Y., 2022. Research on the impact of circular economy on total factor
carbon productivity in China. Environ. Sci. Pollut. Res. 29, 78780–78794.
https://doi.org/10.1007/s11356-022-21314-7
Dasgupta, S., Maanen, N. van, Gosling, S.N., Piontek, F., Otto, C., Schleussner, C.-F.,
2021. Effects of climate change on combined labour productivity and supply:
an empirical, multi-model study. Lancet Planet. Health 5, e455–e465.
https://doi.org/10.1016/S2542-5196(21)00170-4
Du, K., Li, J., 2019. Towards a green world: How do green technology innovations
affect total-factor carbon productivity. Energy Policy 131, 240–250.
https://doi.org/10.1016/j.enpol.2019.04.033
Duan, H., Zhou, S., Jiang, K., Bertram, C., Harmsen, M., Kriegler, E., van Vuuren,
D.P., Wang, S., Fujimori, S., Tavoni, M., Ming, X., Keramidas, K., Iyer, G.,
Edmonds, J., 2021. Assessing China’s efforts to pursue the 1.5°C warming limit.
Science 372, 378–385. https://doi.org/10.1126/science.aba8767
Emrouznejad, A., Yang, G., 2016. A framework for measuring global Malmquist–
Luenberger productivity index with CO2 emissions on Chinese manufacturing
industries. Energy 115, 840–856. https://doi.org/10.1016/j.energy.2016.09.032
Fang, L., Hu, R., Mao, H., Chen, S., 2021. How crop insurance influences agricultural
green total factor productivity: Evidence from Chinese farmers. J. Clean. Prod.
321, 128977. https://doi.org/10.1016/j.jclepro.2021.128977
Fisher, S., Bellinger, D.C., Cropper, M.L., Kumar, P., Binagwaho, A., Koudenoukpo,
J.B., Park, Y., Taghian, G., Landrigan, P.J., 2021. Air pollution and
development in Africa: Impacts on health, the economy, and human capital.
Lancet Planet. Health 5, e681–e688. https://doi.org/10.1016/S2542-
5196(21)00201-1
Gao, Y., Zhang, M., Zheng, J., 2021. Accounting and determinants analysis of China’s
provincial total factor productivity considering carbon emissions. China Econ.
Rev. 65, 101576. https://doi.org/10.1016/j.chieco.2020.101576
Goldemberg, J., 2008. Pumping renewables. Nature 456, 26–27.
https://doi.org/10.1038/twas08.26a
Gyimah, J., Yao, X., Tachega, M.A., Sam Hayford, I., Opoku-Mensah, E., 2022.
Renewable energy consumption and economic growth: New evidence from
Ghana. Energy 248, 123559. https://doi.org/10.1016/j.energy.2022.123559
Hansen, J.E., Sato, M., Simons, L., Nazarenko, L.S., Sangha, I., Kharecha, P., Zachos,
J.C., von Schuckmann, K., Loeb, N.G., Osman, M.B., Jin, Q., Tselioudis, G.,
Jeong, E., Lacis, A., Ruedy, R., Russell, G., Cao, J., Li, J., 2023. Global
warming in the pipeline. Oxf. Open Clim. Change 3, kgad008.
https://doi.org/10.1093/oxfclm/kgad008
He, X., Khan, S., Ozturk, I., Murshed, M., 2023. The role of renewable energy
investment in tackling climate change concerns: Environmental policies for
achieving SDG-13. Sustain. Dev. 31, 1888–1901.
https://doi.org/10.1002/sd.2491
Howell, S.T., 2017. Financing innovation: Evidence from R&D grants. Am. Econ. Rev.
107, 1136–1164. https://doi.org/10.1257/aer.20150808
Hsieh, C.-T., Klenow, P.J., 2009. Misallocation and manufacturing TFP in China and
India. Q. J. Econ. 124, 1403–1448.
https://doi.org/10.1162/qjec.2009.124.4.1403
Huang, J., Pan, X., Guo, X., Li, G., 2018. Health impact of China’s Air Pollution
Prevention and Control Action Plan: an analysis of national air quality
monitoring and mortality data. Lancet Planet. Health 2, e313–e323.
https://doi.org/10.1016/S2542-5196(18)30141-4
Huang, K.G.-L., Huang, C., Shen, H., Mao, H., 2021. Assessing the value of China’s
patented inventions. Technol. Forecast. Soc. Change 170, 120868.
https://doi.org/10.1016/j.techfore.2021.120868
Huang, L., Zou, Y., 2020. How to promote energy transition in China: From the
perspectives of interregional relocation and environmental regulation. Energy
Econ. 92, 104996. https://doi.org/10.1016/j.eneco.2020.104996
Huang, Z., Liao, G., Li, Z., 2019. Loaning scale and government subsidy for promoting
green innovation. Technol. Forecast. Soc. Change 144, 148–156.
https://doi.org/10.1016/j.techfore.2019.04.023
Huo, W., Qi, J., Yang, T., Liu, J., Liu, M., Zhou, Z., 2022. Effects of China’s pilot low-
carbon city policy on carbon emission reduction: A quasi-natural experiment
based on satellite data. Technol. Forecast. Soc. Change 175, 121422.
https://doi.org/10.1016/j.techfore.2021.121422
IEA, 2023. Government Energy Spending Tracker. International Energy Agency, Paris.
Inglesi-Lotz, R., 2016. The impact of renewable energy consumption to economic
growth: A panel data application. Energy Econ. 53, 58–63.
https://doi.org/10.1016/j.eneco.2015.01.003
Kätelhön, A., Meys, R., Deutz, S., Suh, S., Bardow, A., 2019. Climate change
mitigation potential of carbon capture and utilization in the chemical industry.
Proc. Natl. Acad. Sci. 116, 11187–11194.
https://doi.org/10.1073/pnas.1821029116
Kaya, Y., 1989. Impact of carbon dioxide emission control on GNP growth:
Interpretation of proposed scenarios. IPCC Energy and Industry Subgroup,
Response Straegies Working Group, Paris.
Kazemiparkouhi, F., Honda, T., Eum, K.-D., Wang, B., Manjourides, J., Suh, H.H.,
2022. The impact of long-term pm2.5 constituents and their sources on specific
causes of death in a US medicare cohort. Environ. Int. 159, 106988.
https://doi.org/10.1016/j.envint.2021.106988
Kern, F., Rogge, K.S., 2016. The pace of governed energy transitions: Agency,
international dynamics and the global Paris agreement accelerating
decarbonisation processes? Energy Res. Soc. Sci. 22, 13–17.
https://doi.org/10.1016/j.erss.2016.08.016
Khan, S.A.R., Ponce, P., Yu, Z., 2021. Technological innovation and environmental
taxes toward a carbon-free economy: An empirical study in the context of COP-
21. J. Environ. Manage. 298, 113418.
https://doi.org/10.1016/j.jenvman.2021.113418
Kong, D., Zhang, B., Zhang, J., 2022. Higher education and corporate innovation. J.
Corp. Finance 72, 102165. https://doi.org/10.1016/j.jcorpfin.2022.102165
Krugman, P., 1994. The myth of Asia’s miracle. Foreign Aff. 73, 62–78.
https://doi.org/10.2307/20046929
Laursen, K., Salter, A., 2006. Open for innovation: The role of openness in explaining
innovation performance among UK manufacturing firms. Strateg. Manag. J. 27,
131–150. https://doi.org/10.1002/smj.507
Lee, C-C, Wang, F., Lou, R., Wang, K., 2023b. How does green finance drive the
decarbonization of the economy? Empirical evidence from China. Renew.
Energy 204, 671–684. https://doi.org/10.1016/j.renene.2023.01.058
Lee, C-C, Zhang, J., Hou, S., 2023a. The impact of regional renewable energy
development on environmental sustainability in China. Resour. Policy 80,
103245. https://doi.org/10.1016/j.resourpol.2022.103245
Lee, C-C, Lee, C-C, 2022. How does green finance affect green total factor
productivity? Evidence from China. Energy Econ. 107, 105863.
https://doi.org/10.1016/j.eneco.2022.105863
Li, G., Shen, Z., Song, M., Vardanyan, M., 2023b. The role of economic land use
efficiency in promoting green industrial development: Evidence from China.
Ann. Oper. Res. https://doi.org/10.1007/s10479-023-05721-8
Li, L., Li, M., Ma, S., Zheng, Y., Pan, C., 2022. Does the construction of innovative
cities promote urban green innovation? J. Environ. Manage. 318, 115605.
https://doi.org/10.1016/j.jenvman.2022.115605
Li, Z., Wang, M., Wang, Q., 2023a. Job destruction and creation: Labor reallocation
entailed by the clean air action in China. China Econ. Rev. 79, 101945.
https://doi.org/10.1016/j.chieco.2023.101945
Lin, B., Huang, C., 2022. Analysis of emission reduction effects of carbon trading:
market mechanism or government intervention? Sustain. Prod. Consum. 33, 28–
37. https://doi.org/10.1016/j.spc.2022.06.016
Lin, B., Zhu, J., 2019. Determinants of renewable energy technological innovation in
China under CO2 emissions constraint. J. Environ. Manage. 247, 662–671.
https://doi.org/10.1016/j.jenvman.2019.06.121
Liu, F.-H.F., Wang, P., 2008. DEA Malmquist productivity measure: Taiwanese
semiconductor companies. Int. J. Prod. Econ., Special Section on Recent
Developments in the Design, Control, Planning and Scheduling of Productive
Systems 112, 367–379. https://doi.org/10.1016/j.ijpe.2007.03.015
Liu, X., Hua, Y., Liu, X., Yang, L., Sun, Y., 2023a. Design and implementation of
smooth renewable power in cloud data centers. IEEE Trans. Cloud Comput. 11,
85–96. https://doi.org/10.1109/TCC.2021.3076978
Liu, Y., Xie, Y., Zhong, K., 2023b. Impact of digital economy on urban sustainable
development: Evidence from Chinese cities. Sustain. Dev. n/a.
https://doi.org/10.1002/sd.2656
Long, X., Naminse, E.Y., Du, J., Zhuang, J., 2015. Nonrenewable energy, renewable
energy, carbon dioxide emissions and economic growth in China from 1952 to
2012. Renew. Sustain. Energy Rev. 52, 680–688.
https://doi.org/10.1016/j.rser.2015.07.176
Luo, J., Wang, Y., Li, G., 2023. The innovation effect of administrative hierarchy on
intercity connection: The machine learning of twin cities. J. Innov. Knowl. 8,
100293. https://doi.org/10.1016/j.jik.2022.100293
Lyu, Y., Wang, W., Wu, Y., Zhang, J., 2023. How does digital economy affect green
total factor productivity? Evidence from China. Sci. Total Environ. 857,
159428. https://doi.org/10.1016/j.scitotenv.2022.159428
McElroy, M.B., Lu, X., Nielsen, C.P., Wang, Y., 2009. Potential for wind-generated
electricity in China. Science 325, 1378–1380.
https://doi.org/10.1126/science.1175706
Meng, S., Sun, R., Guo, F., 2022. Does the use of renewable energy increase carbon
productivity? ——An empirical analysis based on data from 30 provinces in
China. J. Clean. Prod. 365, 132647.
https://doi.org/10.1016/j.jclepro.2022.132647
Mohajeri, N., Hsu, S.-C., Milner, J., Taylor, J., Kiesewetter, G., Gudmundsson, A.,
Kennard, H., Hamilton, I., Davies, M., 2023. Urban–rural disparity in global
estimation of PM2·5 household air pollution and its attributable health burden.
Lancet Planet. Health 7, e660–e672. https://doi.org/10.1016/S2542-
5196(23)00133-X
Nagababu, G., Srinivas, B.A., Kachhwaha, S.S., Puppala, H., Kumar, S.V.V.A., 2023.
Can offshore wind energy help to attain carbon neutrality amid climate change?
A GIS-MCDM based analysis to unravel the facts using CORDEX-SA. Renew.
Energy 219, 119400. https://doi.org/10.1016/j.renene.2023.119400
Nasir, M.H.A., Genovese, A., Acquaye, A.A., Koh, S.C.L., Yamoah, F., 2017.
Comparing linear and circular supply chains: A case study from the construction
industry. Int. J. Prod. Econ., Closed Loop Supply Chain (CLSC): Economics,
Modelling, Management and Control 183, 443–457.
https://doi.org/10.1016/j.ijpe.2016.06.008
Ni, L., Ahmad, S.F., Alshammari, T.O., Liang, H., Alsanie, G., Irshad, M., Alyafi-
AlZahri, R., BinSaeed, R.H., Al-Abyadh, M.H.A., Abu Bakir, S.M.M.,
Ayassrah, A.Y.A.B.A., 2023. The role of environmental regulation and green
human capital towards sustainable development: The mediating role of green
innovation and industry upgradation. J. Clean. Prod. 421, 138497.
https://doi.org/10.1016/j.jclepro.2023.138497
Oh, D., 2010. A global Malmquist-Luenberger productivity index. J. Product. Anal. 34,
183–197. https://doi.org/10.1007/s11123-010-0178-y
Pan, X., Pu, C., Yuan, S., Xu, H., 2022. Effect of Chinese pilots carbon emission trading
scheme on enterprises’ total factor productivity: The moderating role of
government participation and carbon trading market efficiency. J. Environ.
Manage. 316, 115228. https://doi.org/10.1016/j.jenvman.2022.115228
Peng, B., Zhao, H., Bai, J., Wang, W., 2021. Study on solution of renewable energy
accommodation based on quantitative analysis model, in: 2021 3rd International
Conference on Smart Power & Internet Energy Systems (SPIES). Presented at
the 2021 3rd International Conference on Smart Power & Internet Energy
Systems (SPIES), pp. 414–420.
https://doi.org/10.1109/SPIES52282.2021.9633808
Rahman, M.M., Alam, K., 2021. Clean energy, population density, urbanization and
environmental pollution nexus: Evidence from Bangladesh. Renew. Energy
172, 1063–1072. https://doi.org/10.1016/j.renene.2021.03.103
Rahman, M.M., Velayutham, E., 2020. Renewable and non-renewable energy
consumption-economic growth nexus: New evidence from South Asia. Renew.
Energy 147, 399–408. https://doi.org/10.1016/j.renene.2019.09.007
Rehman, A., Ma, H., Ozturk, I., Radulescu, M., 2022. Revealing the dynamic effects of
fossil fuel energy, nuclear energy, renewable energy, and carbon emissions on
Pakistan’s economic growth. Environ. Sci. Pollut. Res. 29, 48784–48794.
https://doi.org/10.1007/s11356-022-19317-5
Reinsdorf, M., 2005. Measurement of Capital Stocks, Consumption of Fixed Capital,
and Capital Services (Report on a Presentation to the Central American Ad Hoc
Group on National Accounts). Santo Domingo, Dominican Republic.
Ren, S., Li, L., Han, Y., Hao, Y., Wu, H., 2022. The emerging driving force of inclusive
green growth: Does digital economy agglomeration work? Bus. Strategy
Environ. 31, 1656–1678. https://doi.org/10.1002/bse.2975
Sebri, M., Ben-Salha, O., 2014. On the causal dynamics between economic growth,
renewable energy consumption, CO2 emissions and trade openness: Fresh
evidence from BRICS countries. Renew. Sustain. Energy Rev. 39, 14–23.
https://doi.org/10.1016/j.rser.2014.07.033
Shah, W.U.H., Hao, G., Yan, H., Zhu, N., Yasmeen, R., Dincă, G., 2023. Role of
renewable, non-renewable energy consumption and carbon emission in energy
efficiency and productivity change: Evidence from G20 economies. Geosci.
Front. 101631. https://doi.org/10.1016/j.gsf.2023.101631
Shahbaz, M., Zakaria, M., Shahzad, S.J.H., Mahalik, M.K., 2018. The energy
consumption and economic growth nexus in top ten energy-consuming
countries: Fresh evidence from using the quantile-on-quantile approach. Energy
Econ. 71, 282–301. https://doi.org/10.1016/j.eneco.2018.02.023
Sheehan, P., Cheng, E., English, A., Sun, F., 2014. China’s response to the air pollution
shock. Nat. Clim. Change 4, 306–309. https://doi.org/10.1038/nclimate2197
Shi, B., Li, N., Gao, Q., Li, G., 2022. Market incentives, carbon quota allocation and
carbon emission reduction: Evidence from China’s carbon trading pilot policy.
J. Environ. Manage. 319, 115650.
https://doi.org/10.1016/j.jenvman.2022.115650
Shi, R., Gao, P., Su, X., Zhang, X., Yang, X., 2024. Synergizing natural resources and
sustainable development: A study of industrial structure, and green innovation
in Chinese region. Resour. Policy 88, 104451.
https://doi.org/10.1016/j.resourpol.2023.104451
Solow, R.M., 1957. Technical change and the aggregate production function. Rev.
Econ. Stat. 39, 312–320. https://doi.org/10.2307/1926047
Solow, R.M., 1956. A contribution to the theory of economic growth. Q. J. Econ. 70,
65–94. https://doi.org/10.2307/1884513
Song, M., Ma, X., Shang, Y., Zhao, X., 2020. Influences of land resource assets on
economic growth and fluctuation in China. Resour. Policy 68, 101779.
https://doi.org/10.1016/j.resourpol.2020.101779
Song, S., Ding, Y., Li, W., Meng, Y., Zhou, J., Gou, R., Zhang, C., Ye, S., Saintilan,
N., Krauss, K.W., Crooks, S., Lv, S., Lin, G., 2023. Mangrove reforestation
provides greater blue carbon benefit than afforestation for mitigating global
climate change. Nat. Commun. 14, 756. https://doi.org/10.1038/s41467-023-
36477-1
Song, Y., Zhang, B., Wang, J., Kwek, K., 2022. The impact of climate change on
China’s agricultural green total factor productivity. Technol. Forecast. Soc.
Change 185, 122054. https://doi.org/10.1016/j.techfore.2022.122054
Su, Y., Fan, Q., 2022. Renewable energy technology innovation, industrial structure
upgrading and green development from the perspective of China’s provinces.
Technol. Forecast. Soc. Change 180, 121727.
https://doi.org/10.1016/j.techfore.2022.121727
Sun, Y., Ben Belgacem, S., Khatoon, G., Nazir, F., 2023. Impact of environmental
taxation, green innovation, economic growth, and renewable energy on green
total factor productivity. Gondwana Res.
https://doi.org/10.1016/j.gr.2023.10.016
Sun, Y., Zhao, Z., Yang, M., Jia, D., Pei, W., Xu, B., 2020. Overview of energy storage
in renewable energy power fluctuation mitigation. CSEE J. Power Energy Syst.
6, 160–173. https://doi.org/10.17775/CSEEJPES.2019.01950
Tauseef Hassan, S., Khan, D., Awais Baloch, M., Bui, Q., Hashim Khan, N., 2023. The
heterogeneous impact of geopolitical risk and environment-related innovations
on greenhouse gas emissions: The role of nuclear and renewable energy in the
circular economy. Gondwana Res. https://doi.org/10.1016/j.gr.2023.08.016
Tian, P., Lin, B., 2017. Promoting green productivity growth for China’s industrial
exports: Evidence from a hybrid input-output model. Energy Policy 111, 394–
402. https://doi.org/10.1016/j.enpol.2017.09.033
Tone, K., 2003. Dealing with Undesirable Outputs in DEA: A Slacks-based Measure
(SBM) Approach, in: GRIPS Research Report Series Ⅰ-2003-0005.
Wang, C., Shen, J., Liu, Y., 2023a. Hukou transfer intention of rural migrants with
settlement intention in China: How cities’ administrative level matters. J. Rural
Stud. 99, 1–10. https://doi.org/10.1016/j.jrurstud.2023.01.022
Wang, G., Bai, L., Chao, Y., Chen, Z., 2023c. How do solar photovoltaic and wind
power promote the joint poverty alleviation and clean energy development: An
evolutionary game theoretic study. Renew. Energy 218, 119296.
https://doi.org/10.1016/j.renene.2023.119296
Wang, M., Xu, M., Ma, S., 2021. The effect of the spatial heterogeneity of human
capital structure on regional green total factor productivity. Struct. Change
Econ. Dyn. 59, 427–441. https://doi.org/10.1016/j.strueco.2021.09.018
Wang, Q., Wang, L., 2020. Renewable energy consumption and economic growth in
OECD countries: A nonlinear panel data analysis. Energy 207, 118200.
https://doi.org/10.1016/j.energy.2020.118200
Wang, W., Yuan, B., Sun, Q., Wennersten, R., 2022. Application of energy storage in
integrated energy systems — A solution to fluctuation and uncertainty of
renewable energy. J. Energy Storage 52, 104812.
https://doi.org/10.1016/j.est.2022.104812
Wang, Y., Wang, R., Tanaka, K., Ciais, P., Penuelas, J., Balkanski, Y., Sardans, J.,
Hauglustaine, D., Liu, W., Xing, X., Li, J., Xu, S., Xiong, Y., Yang, R., Cao, J.,
Chen, J., Wang, L., Tang, X., Zhang, R., 2023b. Accelerating the energy
transition towards photovoltaic and wind in China. Nature 619, 761–767.
https://doi.org/10.1038/s41586-023-06180-8
Wei, J., Li, Z., 2019. ChinaHighPM2.5: Big Data Seamless 1 km Ground-level PM2.5
Dataset for China. https://doi.org/10.5281/zenodo.6398971
Wei, L., Lin, B., Zheng, Z., Wu, W., Zhou, Y., 2023. Does fiscal expenditure promote
green technological innovation in China? Evidence from Chinese cities.
Environ. Impact Assess. Rev. 98, 106945.
https://doi.org/10.1016/j.eiar.2022.106945
Williams, J.H., DeBenedictis, A., Ghanadan, R., Mahone, A., Moore, J., Morrow, W.R.,
Price, S., Torn, M.S., 2012. The technology path to deep greenhouse gas
emissions cuts by 2050: The pivotal role of electricity. Science 335, 53–59.
https://doi.org/10.1126/science.1208365
Xu, P., Ye, P., Jahanger, A., Huang, S., Zhao, F., 2023. Can green credit policy reduce
corporate carbon emission intensity: Evidence from China’s listed firms. Corp.
Soc. Responsib. Environ. Manag. 30, 2623–2638.
https://doi.org/10.1002/csr.2506
Xu, T., Kang, C., Zhang, H., 2022. China’s efforts towards carbon neutrality: Does
energy-saving and emission-reduction policy mitigate carbon emissions? J.
Environ. Manage. 316, 115286. https://doi.org/10.1016/j.jenvman.2022.115286
Yagi, H., Hesiletu, Hara, M., Nishio, F., 2010. Estimation of night light from the
DMSP/OLS, in: International Archives of the Photogrammetry, Remote
Sensing and Spatial Information Science. Kyoto, Japan.
Yang, Z., 2019. Increasing returns to scale in energy-intensive sectors and its
implications on climate change modeling. Energy Econ. 83, 208–216.
https://doi.org/10.1016/j.eneco.2019.06.011
Yasmeen, R., Yao, X., Ul Haq Padda, I., Shah, W.U.H., Jie, W., 2022. Exploring the
role of solar energy and foreign direct investment for clean environment:
Evidence from top 10 solar energy consuming countries. Renew. Energy 185,
147–158. https://doi.org/10.1016/j.renene.2021.12.048
You, Z., Feng, Z., Yang, Y., 2018. Relief Degree of Land Surface Dataset of China
(1km). https://doi.org/10.3974/geodb.2018.03.16.V1
Zahoor, A., Mehr, F., Mao, G., Yu, Y., Sápi, A., 2023. The carbon neutrality feasibility
of worldwide and in China’s transportation sector by E-car and renewable
energy sources before 2060. J. Energy Storage 61, 106696.
https://doi.org/10.1016/j.est.2023.106696
Zarrin, M., Brunner, J.O., 2023. Analyzing the accuracy of variable returns to scale data
envelopment analysis models. Eur. J. Oper. Res. 308, 1286–1301.
https://doi.org/10.1016/j.ejor.2022.12.015
Zeng, S., Jin, G., Tan, K., Liu, X., 2023. Can low-carbon city construction reduce
carbon intensity？Empirical evidence from low-carbon city pilot policy in
China. J. Environ. Manage. 332, 117363.
https://doi.org/10.1016/j.jenvman.2023.117363
Zhang, J., Lu, G., Skitmore, M., Ballesteros-Pérez, P., 2021. A critical review of the
current research mainstreams and the influencing factors of green total factor
productivity. Environ. Sci. Pollut. Res. 28, 35392–35405.
https://doi.org/10.1007/s11356-021-14467-4
Zhang, S., Chen, W., 2022a. Assessing the energy transition in China towards carbon
neutrality with a probabilistic framework. Nat. Commun. 13, 1–15.
https://doi.org/10.1038/s41467-021-27671-0
Zhang, S., Chen, W., 2022b. China’s energy transition pathway in a carbon neutral
vision. Engineering 14, 64–76. https://doi.org/10.1016/j.eng.2021.09.004
Zhang, Y.-J., Da, Y.-B., 2015. The decomposition of energy-related carbon emission
and its decoupling with economic growth in China. Renew. Sustain. Energy
Rev. 41, 1255–1266. https://doi.org/10.1016/j.rser.2014.09.021
Zhang, Z., Ghazali, S., Miceikienė, A., Zejak, D., Choobchian, S., Pietrzykowski, M.,
Azadi, H., 2023. Socio-economic impacts of agricultural land conversion: A
meta-analysis. Land Use Policy 132, 106831.
https://doi.org/10.1016/j.landusepol.2023.106831
Zhao, J., Dong, K., Dong, X., Shahbaz, M., 2022a. How renewable energy alleviate
energy poverty? A global analysis. Renew. Energy 186, 299–311.
https://doi.org/10.1016/j.renene.2022.01.005
Zhao, M., Sun, T., 2022. Dynamic spatial spillover effect of new energy vehicle
industry policies on carbon emission of transportation sector in China. Energy
Policy 165, 112991. https://doi.org/10.1016/j.enpol.2022.112991
Zhao, N., You, F., 2020. Can renewable generation, energy storage and energy efficient
technologies enable carbon neutral energy transition? Appl. Energy 279,
115889. https://doi.org/10.1016/j.apenergy.2020.115889
Zhao, X., Nakonieczny, J., Jabeen, F., Shahzad, U., Jia, W., 2022b. Does green
innovation induce green total factor productivity? Novel findings from Chinese
city level data. Technol. Forecast. Soc. Change 185, 122021.
https://doi.org/10.1016/j.techfore.2022.122021
Zhou, M., 2000. The economic center, regional disparities and coordinated
development. Soc. Sci. China 42-53+206.
Zhuo, Z., Du, E., Zhang, N., Nielsen, C.P., Lu, X., Xiao, J., Wu, J., Kang, C., 2022.
Cost increase in the electricity supply to achieve carbon neutrality in China. Nat.
Commun. 13, 1–13. https://doi.org/10.1038/s41467-022-30747-0
Appendix
Fig. A1 The balance of matching variables after PSM
Note: This figure demonstrates the balance of matching variables after PSM, suggesting a more
comparable sample after matching.
Table A1 The explanation of heterogeneity
Expenditure on S&T
The proportion of S&T
expenditure in the total
expenditure
(1)
(2)
PCEA×Post×South
-0.3715***
-0.1325
(0.1050)
(0.1097)
PCEA×Post
-0.2227***
-0.4525***
(0.0653)
(0.0793)
Controls
YES
YES
City FE
YES
YES
Year FE
YES
YES
Observations
2,588
2,589
Adj. R-squared
0.923
0.851
Note: Robust standard errors clustered at the city level are reported in parentheses.
