Cities emit extensive carbon emissions, which are considered a major contributor to the severe issue of climate change. Various low-carbon development programs have been initiated at the city level worldwide to address this problem. These practices are invaluable in promoting the development of low-carbon cities. Therefore, an effective approach should be developed to help decision makers select the best practices from previous experience on the basis of the impact features of carbon emission and city context features. This study introduces a case-based reasoning methodology for a specific city to select the best practices as references for low-carbon city development. The proposed methodology consists of three main components, namely, case representation, case retrieval, and case adaption and retention. For city representation, this study selects city context features and the impact features of carbon emission to characterize and represent a city. The proposed methodology is demonstrated by applying it to the selection of the best practices for low-carbon development of Chengdu City in Sichuan Province, China.
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This study is largely based on quantitative method. The index of China Green Low-Carbon City Index (CGLCCI) is developed and applied to 115 Chinese cities. The GCLCCI includes 23 key indicators across seven dimensions -- economic, energy, industry, buildings, transportation, environment and land use, climate policy and outreach. This paper applied the CGLCCI to track and benchmark green and low-carbon development status of 115 Chinese cities in 2015. Analysis using the CGLCCI relied on available government data sources, and a standardized method to calculate overall city scores.
The results showed that the low-carbon transition in Chinese cities is still in its early stages. Compared to the best practices benchmarks, the selected cities scored from the lowest score 28.4 to the highest score of 70.8 in 2015 (full score is 100). The paper also identified three top-performing cities in different stages of development and analyzed how they achieved high scores across the index categories. Creating a green low-carbon index that relies on publicly available data in China, and regularly evaluating city performance can encourage Chinese cities to learn best practices from each other, and to strengthen their goals and implementation efforts, are essential to spur China towards a green low-carbon transition.
Porter Hypothesis holds that proper environmental regulations can urge enterprises to develop technological innovation, which lowers the production costs and offsets compliance costs [22]. The LCCP policy used administrative methods and tax incentives to stimulate enterprises to develop low-carbon technologies [23]. Additionally, the policy adopted various ways to subsidize related enterprises, such as low-carbon development funds, investment subsidies, loan interest discounts, direct rewards, and project management fee subsidies, to expand their R&D expenditures on low-carbon technology. Technological innovations lead to higher carbon productivity, which not only compensates for the compliance cost but also makes the enterprises generate fewer carbon emissions than those not investing in low-carbon technologies [24]. Additionally, Gong, Liu [25] found that the LCCP policy significantly promoted foreign direct investment. Technological innovation has spillover effects because foreign enterprises possessing advanced technologies spread greener production technologies to host countries to help them to improve their environmental protection levels. In summary, we believe that the LCCP policy can promote low-carbon technological innovation which is beneficial to low-carbon economic transition. Therefore, we propose the first hypothesis:
As mentioned above, the LCCP policy does not have specific quantitative targets, financial supports, and compensation rules, meaning that local governments can freely choose implementation paths and tools. Generally, local governments use three types of policy tools to build low-carbon cities, including command-mandatory tools, market-economic tools, and voluntary tools [26].
Command-mandatory tools used in the LCCP policy mainly include outdated production elimination, emission control standards for motor vehicles, low energy consumption for green buildings, vehicle emission standards. For example, Tianjin City, one of the eight first pilot cities, participated in the National Energy Conservation Plan, so the Tianjin government required 211 local enterprises to save 4.86 million tons of standard coal. Market-economic tools applied by the LCCP policy mainly consist of low-carbon subsidies, preferential interest loans for low-carbon programs, carbon emissions trading, tax incentives. For instance, in 2011 two provinces, namely Hubei Province and Guangdong Province and five municipalities, namely Beijing City, Shanghai City, Tianjin City, Chongqing City, and Shenzhen City conduct the Carbon Emission Trading Pilot Scheme (ETPS). Voluntary tools adopted in the LCCP policy mainly comprise low-carbon transportation programs, low-carbon industrial park programs, carbon monitoring. For example, Tianjin has established a green building certification system and standards. Additionally, Hangzhou City has adopted the low-carbon product certification system by using ISO 14064 and PAS 2050 and encouraged local enterprises to reduce carbon emissions per unit product.
The first core variable is the low-carbon economic transition as the explained variable. This article used carbon productivity (CP) to measure the level of low-carbon economic transition based on Wang, Chen [11]. Carbon productivity refers to the level of GDP output per unit of carbon dioxide, specifically the ratio of GDP to carbon dioxide emissions. Carbon productivity is the most applied in existing studies to describe the transition of a low-carbon economy [11]. It is a common dilemma for developing countries to keep the balance between ecosystem and economic growth. Working on carbon productivity can make greater economic growth with lower carbon emissions [29]. Therefore, we adopted carbon productivity as an indicator of the economic low-carbon transition.
The second core variable is low-carbon innovation (LCI) as a mediation variable. Patent authorization standards are objective and stable, so the number of patents can reflect the level of innovation [31]. The patients classified as Y02 are green technologies and applications for mitigating or adapting to climate change in the patent classification catalog jointly issued by the European Patent Office and the US Patent Office [32]. This article regarded the patents classified as Y02B, Y02C, Y02D, Y02E, Y02P, Y02T, and Y02W as low-carbon innovation patients, and adopted their total number as a low-carbon innovation indicator for each city [33]. For the consideration of heteroscedasticity, this article takes the logarithm of low-carbon innovation (lnlci).
Total population (POP) is the number of permanent residents in the city. [35] believe that the influence of population size on carbon emissions cannot be ignored, and population growth leads to an increase in total carbon emissions. Therefore, we use the total population as a control variable.
Infrastructure (IF) is the area per capita of urban road areas. Zhang [8] believed that good infrastructure can not only bring a broad market but also enhance inter-regional communication. And, convenient transportation is conducive to attracting talents, capital, and other production factors. The influx of production factors and the expansion of the market have jointly promoted the transformation of the regional industrial structure and changed the regional carbon emission pattern. Therefore, we use the infrastructure as a control variable.
For the consideration of heteroscedasticity, this article takes the logarithm of total population (lnPOP), economic development level (lnEL), and infrastructure (lnIF). The descriptive statistics of the main variables are in Table 1.
Porter Hypothesis holds that proper environmental regulations can urge enterprises to develop technological innovation, which lowers the production costs and ultimately benefit foreign trade [22]. The LCCP policy used administrative methods and tax incentives to stimulate enterprises to develop low-carbon technologies [23]. Additionally, the policy adopted various ways to subsidize related enterprises, such as low-carbon development funds, investment subsidies, loan interest discounts, direct rewards, and project management fee subsidies, to expand their R&D expenditures on low-carbon technology. Technological innovations lead to higher carbon productivity, which not only compensates for the compliance cost but also makes the enterprises generate fewer carbon emissions than those not investing in low-carbon technologies [24].
Eq (3) is a benchmark DID model. In Eq (4), lnLCPit as the explained variable represents the low-carbon innovation of provinces i in time t. And Eq (5) is to add lnLCIit to Eq (3). The mediation effect is tested by stepwise regression.
In the first place, we discuss regression coefficient α1 in Eq (3). If α1 is not significant, the causal relationship between the LCCP policy and low-carbon economic transition is weak. So, the mediation effect test ends. But if α1 is significant, we continue to construct Eq (4) to discuss whether the LCCP policy affects the low-carbon innovation. If β1 is not significant, the causal relationship between the policy and the low-carbon innovation is weak. So, the mediation effect test ends. But if β1 is significant, we continue to construct Eq (5) to discuss whether there is a mediation effect on low-carbon innovation. In Eq (5), if the regression coefficients both λ1 and λ2 are significant and λ1 is closer to 0 than α1, the low-carbon innovation is a mediation variable for the LCCP policy to influence the low-carbon transition. And the mediation effect is partial. If the regression coefficient λ1 is not significant, but the regression coefficient λ2 is significant, the low-carbon innovation is also a mediation variable for the LCCP policy to influence low-carbon transition. In this case, the mediation effect is full. If neither of them is significant, low-carbon innovation is not a mediation variable for the LCCP policy to influence the low-carbon transition. 2ff7e9595c
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