Choice of technological change for China's low-carbon development: Evidence from three urban agglomerations

Jia, Pinrong; Li, Ke; Shao, Shuai

doi:10.1016/j.jenvman.2017.08.052

Cited by 74 publications

(36 citation statements)

References 42 publications

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“…First, before analyzing the impact of technological innovation on carbon emissions and energy consumption, the appropriate linear regression models (mixed effect model, fixed effect model or random effect model) were chosen by F test and hausman test to obtain accurate results. By adopting linear regression models, it is verified that technological innovation effectively saves energy and reduces carbon emissions for the whole China, which proves again the important role of technological innovation in low-carbon transformation accepted by many scholars [6], [11], [17], [21]. Besides, this paper illustrates that the effect of China's innovationdriven development strategy on energy saving and carbon emissions are obvious and successful, and technological innovation tends to be environmentally friendly.…”

Section: Discussionsupporting

confidence: 52%

“…In China's three urban agglomerations, namely, ''Beijing-Tianjin-Hebei'', ''Yangtze River Delta'', and ''Pearl River Delta'', technological change (measured by the relative marginal output of two factors) is conducive to electricity saving, but it does not present an emission-reduction effect [21]. The study of Samargandi [22] showed that technological innovation (measured by total number of patent application) is insignificant in helping to reduce CO 2 emissions in the case of Saudi Arabia.…”

Section: Literature Review a The Effect Of Technological Innovatmentioning

confidence: 99%

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Is Technological Innovation Effective for Energy Saving and Carbon Emissions Reduction? Evidence From China

Zhang

Yin

2020

IEEE Access

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Technological innovation is increasingly becoming an important way to develop a lowcarbon economy. Recently, exploring the driving path of low-carbon development has become the hot topic, however, the role of energy consumption in the carbon-reduction effect of technological innovation has been ignored. Based on China's provincial-level panel data from 2005 to 2016, this paper investigates the effect of technological innovation on energy saving and carbon emissions reduction and the role of energy consumption in the influencing process by using linear regression model, mediating effect model and threshold regression model. The results show that technological innovation has effectively promoted energy saving and carbon emissions reduction in China, with different effect coefficients between the three areas of China. Moreover, energy consumption plays a significant partial mediating role in the effect of technological innovation on carbon emissions reduction. Finally, the effect of technological innovation on carbon emissions is limited by energy consumption. Low energy consumption significantly promotes a reduction in carbon emissions through technological innovation. Once energy consumption exceeds a critical level, the promoting effect of technological innovation on carbon emissions reduction will turn into the inhibiting effect. Based on these findings, some policy recommendations are put forward to accelerate the low-carbon transformation of the economy. INDEX TERMS Technological innovation, energy saving, carbon emissions reduction, threshold regression model, China.

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Section: Discussionsupporting

confidence: 52%

Section: Literature Review a The Effect Of Technological Innovatmentioning

confidence: 99%

Is Technological Innovation Effective for Energy Saving and Carbon Emissions Reduction? Evidence From China

Zhang

Yin

2020

IEEE Access

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“…The injection of foreign direct investment and technical knowledge has resulted in the diversification of industries, shifting from the dominant manufacturing to emerging tertiary (service) and quaternary industries [1,43,79,89,90]. For example, Li et al [84] highlights that the city of Wuxi has grown into the most significant production and export hub of the photovoltaic industry in China, where manufacturing and related industries are clustered.…”

Section: Integrationmentioning

confidence: 99%

“…Several scholars consider these emerging industries as disruptive technological innovation [76]. This is because, they upgrade the urban industrial structure to provide a diverse range of industries, new markets and value networks that are conducive to energy savings, replicability, and industrial integration [89]. Supporting this assertion is the study of Gu et al [91], which involved 132 LC enterprises in Liaoning.…”

Section: Integrationmentioning

confidence: 99%

Sustainability of Low Carbon City Initiatives in China: A Comprehensive Literature Review

Hunter

Sagoe²,

Vettorato

et al. 2019

Sustainability

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Low carbon city (LCC) has emerged as the latest sustainable urbanism strategy in China as a response to climate change impacts. Yet, minimal scholarships have explored the sustainability of the urban planning model towards understanding the complexity of the components. Using a two-step triangulation approach, this paper presents a structured overview of the LCC initiative in China as it relates to the transition to a sustainability paradigm. The data collection approach includes a comprehensive review of 238 articles on LCC to identify and categorize LCC components. Furthermore, discourse and framing analysis was used to develop and synthesize a conceptual framework for assimilating the components into four core sustainable development principles: Integration, implementation, equity, and scalability and replicability. The results indicate that LCC development in China is bias towards economic and environmental technological innovations and strategies. Additionally, several critical sustainability issues of LCC pilots were identified. These include a lack of social equity planning concerns for the most vulnerable population, dearth of social reforms that cater to lifestyle and behavioral change, top-down planning and decision-making processes, a technocratic rationalization planning approach, inconsistent LCC targets on inter-generational justice concerns, absence of an effective national "sharing and learning" city-city network system, and several barriers to implementation. We conclude that the applied theoretical and conceptual inquiry into the field of LCC is pertinent to mitigate climate change and achieve sustainable urban development.Sustainability 2019, 11, 4342 2 of 37 consumption [14][15][16][17]. This increasing energy consumption and coal dependency of the urban energy system has increased greenhouse gas (GHG) emissions, as China has become the world's largest energy consumer and carbon emitter [18,19]. A recent report by the National Bureau of Statistics [20] suggests that despite a decline in GHG emissions between 2013 and 2016, emissions grew by 2.3% in 2018 due to a surge in urban construction. Therefore, after three decades of rapid physical and socio-spatial modernization, the country is facing significant negative externalities [16]. Local environmental pollution [6,16,[21][22][23], ecological deterioration [24], and energy shortage [21,25] have created health risks [26], have affected people's quality of life [27], and resulted in substantial economic losses [28] in megacities such as Tianjin and Beijing [29]. Concomitantly, China's cities are vulnerable to climate change impacts [17,30], as some cities have inadequate seawall defenses in low-lying areas [5], and are therefore exposed to flooding and extreme rainstorms [23]. In addition to this, some experience droughts from reduced precipitation, which can be attributed to low urban institutional capacity that creates limitations in climate governance and planning [31]. These complex and intractable problems have heralded a call for action, t...

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“…Some people believe that climate change is largely caused by human activities. Among these human factors, the most noteworthy is the increase of CO2 concentration emitted by burning fossil fuels, followed by the grasp and grasp it will be a difficult point [5,6].…”

Section: Introductionmentioning

confidence: 99%

Energy conversion path and optimization model in COVID-19 under low carbon constraints based on statistical learning theory

Wang

Yü

Gao

et al. 2020

IFS

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This paper uses statistical learning theory and big data analysis to study the energy consumption structure of China from qualitative and quantitative aspects during COVID-19. According to the domestic and foreign scholars’ research on the optimization of energy consumption structure, the carbon emission factor is considered in the optimization of energy consumption structure. Taking the minimum energy consumption cost and carbon dioxide emission as the objective function, the carbon dioxide emission is taken as the objective function, and the total energy consumption and various energy consumption proportions as the constraint conditions, the multi-objective planning method is used to evaluate the energy consumption structure of China. The optimization model of source consumption structure is analyzed, and the medium and long-term energy transformation path and optimization model under low-carbon constraints are studied. Combined with the experimental algorithms related to big data, it is concluded that China’s economic development mainly depends on a large amount of energy consumption during the COVID-19 period. On this basis, some suggestions are put forward to realize the sustainable development of China’s economy and energy.

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Choice of technological change for China's low-carbon development: Evidence from three urban agglomerations

Cited by 74 publications

References 42 publications

Is Technological Innovation Effective for Energy Saving and Carbon Emissions Reduction? Evidence From China

Is Technological Innovation Effective for Energy Saving and Carbon Emissions Reduction? Evidence From China

Sustainability of Low Carbon City Initiatives in China: A Comprehensive Literature Review

Energy conversion path and optimization model in COVID-19 under low carbon constraints based on statistical learning theory

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