Urban Low Carbon Research Articles

Strategizing Low Carbon Urban Planning Through Environmental Impact Assessment by Artificial Intelligence Driven Carbon Foot-print Forecasting

Addressing the associated rise in Carbon Emissions (CE) as smart cities expand becomes paramount. Effective low-carbon urban planning demands robust, precise assessments. This research introduces a cutting-edge solution via an Artificial Intelligence (AI) -driven Carbon Footprint (CF) impact assessment. A detailed dataset, collected over 3 years, was harnessed to gather insights into vital urban factors, including CE, Energy Consumption (EC) patterns, variations in land use, transportation dynamics, and changes in air quality. The cornerstone of this research is developing the Multi-modal Stacked VAR-LSTM model. This model proposes to provide accurate CF predictions for urban environments by merging the capabilities of Vector Autoregression (VAR) with Long Short-Term Memory (LSTM) neural networks. The process encompasses dedicated assessments for each data segment, harnessing VAR to delineate interdependencies and refining these predictions with the LSTM network using the residuals from the VAR analysis. By interweaving AI-driven carbon footprint impact assessments into the urban planning discourse, this study underscores the vast potential in sculpting future urban development strategies that are sustainable and sensitive to carbon impact.

Digital Economy and Urban Low-Carbon Transition: Theoretical Model and New Mechanisms

Digital Economy and Urban Low-Carbon Transition: Theoretical Model and New Mechanisms

Response of Low Carbon Level to Transportation Efficiency in Megacities: A Case Study of Beijing, China

Global warming caused by massive carbon dioxide emissions can lead to a chain of ecological disasters. As one of the main sources of carbon emissions, transportation is of great significance, and the evaluation of its connections with carbon emissions is necessary to achieve “carbon neutrality”. Taking Beijing as an example, this study evaluated traffic efficiency (TE) by utilizing principal component analysis and fuzzy comprehensive evaluation. Using the Tapio decoupling model and coupling coordination degree model, the corresponding relationship between urban low carbon level (LCL) and TE was explored. The results showed the following: (1) The total carbon emission (CE) level exhibited fluctuating variation from increasing to decreasing. The carbon emission intensity (CEI) continued to slow down, and the rapid growth of population density played a key role in low-carbon development. (2) The traffic operations continually showed a positive trend in development. TE increased from a step-like to a slow shape, until it declined in 2020 due to the pandemic. (3) TE and LCL both developed from low coordination to an extreme level of coordination. Per capita carbon emission (CEP) and TE presented an inverted U-shaped curve; meanwhile, with increases in TE, the decline in CEI slowed. In addition, the weak decoupling of TE changed to become strong, due to CE and CEP, and maintained a strong decoupling state from CEI. (4) There is a necessity for the rational planning of land use for transportation infrastructure, the encouragement of a combination of public and private transportation, and the strengthening of the maintenance of the relative infrastructure and the management of traffic behaviors to attain a win–win situation. The results provide a reference for optimizing the traffic structure to achieve “carbon neutrality”.

Spatiotemporal Patterns and Driving Factors of Green and Low-Carbon Urbanization in the Yangtze River Delta Region, China

Spatiotemporal Patterns and Driving Factors of Green and Low-Carbon Urbanization in the Yangtze River Delta Region, China

Analysis on the Effect of Financial Development on Urban Low-Carbon Transition Based on STIRPAT Model

Analysis on the Effect of Financial Development on Urban Low-Carbon Transition Based on STIRPAT Model

Evaluation method for urban low carbon transportation development based on integrated weighting

Evaluation method for urban low carbon transportation development based on integrated weighting

Evaluation method for urban low carbon transportation development based on integrated weighting

Evaluation method for urban low carbon transportation development based on integrated weighting

Evolution Characteristics and Main Influencing Factors of Carbon Dioxide Emissions in Chinese Cities from 2005 to 2020

Based on the carbon emission database of the China Urban Greenhouse Gas Working Group, this paper analyzed the spatiotemporal evolution characteristics and main influencing factors of urban carbon dioxide emissions in China using ArcGIS spatial analysis and SPSS statistical analysis methods, in order to provide a reference for the formulation of the national “double-carbon” strategy and the construction of low-carbon urbanization. The results showed that (1) the urban carbon dioxide emissions in China exhibit a “point-line-area” spreading spatial grid. Carbon dioxide emissions form a planar emission pattern surrounded by the Beijing–Tianjin–Hebei urban agglomeration, Yangtze River Delta urban agglomeration, and Central Plains urban agglomeration. A high per capita and high-intensity emission belt from Xinjiang to Inner Mongolia has been formed. (2) The proportion of industrial emissions continues to decrease, and the range of high industrial emissions has gradually crossed the “Hu Huan-yong Line”, spreading from eastern China to the whole country. The emissions from transportation, the service industry, and households have become new growth points, and high-value emissions from households have also shown a nationwide spreading trend. (3) The main factors influencing the spatial distribution of carbon dioxide emissions are urbanization, the economy, industry, investment, and household energy consumption.

Passenger Flow Forecast for Low Carbon Urban Transport Based on Bi-Level Programming Model

<p>In the context of low-carbon city development, this paper further implements a rail transit passenger flow forecasting method to optimize energy consumption by combining the MMA allocation model with a two-tier planning model for carbon emission control. Through this approach, this paper not only fills the gap of rail transportation planning theories and methods compatible with low-carbon city development, but also emphasizes the importance of energy consumption in transportation planning. Based on a two-tier planning model, this paper considers the Starkberg game between multi-modal and multi-type passenger flow forecasting of rail transit and CO2 emissions of integrated transportation systems. By optimizing the allocation of users in the transportation network from the perspective of both users and planners, while optimizing the CO2 emissions of the integrated transportation system, the dual optimization of energy consumption and environmental benefits is achieved. The method will also be tested in Shanghai, and this paper will comparatively study three different carbon emission control schemes. By assigning passenger flows to the entire transportation system network in Shanghai based on information from the Fourth Integrated Transport Survey, including passenger flows on each road in the road network, passenger flows on each rail line, and characteristic indicators, this paper provides a reliable data base. This study provides a solid foundation for planning the layout of rail transit in a low-carbon mode and makes a positive contribution to sustainable urban development by optimizing energy consumption.</p> <p> </p>

Exploring the coupling and decoupling relationship of urbanization and carbon emissions in China.

Burgeoning urbanization is a defining challenge for global carbon emissions mitigation in the coming decades. In this context, achieving low-carbon urbanization remains an urgent issue that demands prompt solutions. The coupling and decoupling relationships between urbanization and carbon emissions play an important role in the coordination of urbanization development and carbon emissions reduction, which has rarely been explored in existing studies, especially in China at the county level. To address this gap, the coupling and decoupling relationship between the urbanization level (UL) and carbon emissions density (CED) was explored using an improved coupling degree model and the Tapio decoupling method in China at the county level from 2000 to 2020. The results showed that the UL and CED of China both exhibited increasing trends, and the spatial distribution was quite similar, with the UL increasing from 0.018 in 2000 to 0.028 in 2020 and the CED increasing from 95.163 ton/km2 in 2000 to 295.746 ton/km2 in 2020. The spatial distribution of hotspots in the UL change differed with time, whereas that in the CED change was relatively stable. However, both of themwere concentrated in eastern China. The coupling degrees of the UL and CED in China were 0.348, 0.355, 0.369, 0.370, and 0.338 in 2000, 2005, 2010, 2015, and 2020, respectively, with the moderately unbalanced type (>35%) being dominant at the county level and mainly scattered in eastern China. The changes in the spatial distribution patterns of the 10 subcategories were quite limited, with the systematically balanced type being dominant (89%). The decoupling types of the UL and CED during 2000-2005, 2005-2010, and 2010-2015 were weak decoupling, while those in 2015-2020 were expansive negative decoupling. At the county level, the most significant transition occurred between expansive negative decoupling, strong decoupling, and strong negative decoupling. The proportion of strong decoupling type counties peaked in 2015-2020 (70.86%), whereas that of the strong negative decoupling type counties remained high (17.55%), scattering the country. These findings can advance policy enlightenment of low-carbon urbanization and green development for China against the backdrop of "30·60 dual carbon" goal.

Matching degree evaluation between new urbanization and carbon emission system in China: a case study of Anhui Province in China

In order to reveal the relationship between new urbanization and carbon emission to provide reference opinions for the construction of low-carbon urbanization, an evaluation system between new urbanization and carbon emission was constructed. Then their matching degree relationship was analyzed by coupling coordination degree model based on the data from 2012 to 2021 in Anhui Province, and their development trend from 2023 to 2032 was predicted by gray prediction model. The results show that: (1) New urbanization and carbon emission have the co-trend effect, and the consistency of core impact factors is relatively significant. Among them, the level of new urbanization increases from 0.058 in 2012 to 0.699 in 2021 and carbon emission development increases from 0.023 in 2012 to 0.165 in 2021, which both showing an upward trend. Meanwhile, social urbanization and population carbon emission are the core influencing factors. (2) The coupling coordination degree between new urbanization and carbon emission is low, but the synergy trend is optimistic and there is a large room for improvement. Among them, the coupling coordination coefficient of the coupling system rises from 0.136 in 2012 to 1.412 in 2021 (antagonistic phase), and then reaches 0.820 by 2032 (highly coordinated phase) by forecast. It shows that their current development is unbalanced, but the development trend is good, and there is a chance for improvement. This paper deepens the understanding of the logical correlation between new urbanization and carbon emission, and the following views are formed: (1) Low-carbon development is still the mainstream of new urbanization; (2) The coordination development of new urbanization and carbon emission reduction should be strengthened.

From Policy to Implementation—An Analytic Network Process (ANP)-Based Assessment Tool for Low Carbon Urban and Neighborhood Planning

To achieve the goals of carbon peaking, a national policy instrument for carbon peaking in the building and construction sector has been launched by the Chinese MOHURD (30 June 2022). We have developed an actionable framework for implementing these policy objectives. The framework was designed by classifying and prioritizing selected strategic government recommendations in the form of an interactive indicator system and tool for evaluating the quality of low-carbon urban and neighborhood planning actions based on the decarbonation principles of carbon emission reduction and carbon capture. The analytic network process (ANP) was applied for processing the interactions and prioritizing the indicators (23 in total for the two principles applied). A scorecard was designed for assessing low-carbon urban and neighborhood planning strategies and technologies. The practical implementation of the tool was then tested with two real planning cases, one from a fourth-tier Chinese city and another from a high-density city. The applicability of the tool is further discussed by comparing it with well-developed international assessment tools in other contexts. This article contributes to the literature by first initiating research on the use of this evaluative tool for low-carbon planning and secondly by demonstrating how researchers can convert policies into practical implementations.

The Relationship between Environmental Regulation, Industrial Transformation Change and Urban Low-Carbon Development: Evidence from 282 Cities in China.

Environmental regulation (ER) plays an important role in urban low-carbon development (ULCD). First of all, we evaluate the ULCD level of 282 cities in China from 2005 to 2020 by constructing an index group and entropy method. Two panel models are then used to test the spillover effects and threshold effects of ER and industrial structure on ULCD. The results show that the ULCD level of most cities is still in grade III (0.27–0.38) or IV (0.38–0.49), and the level of central-western cities is generally lower than that of eastern cities. Furthermore, the spillover effect of ER and industrial structure upgrading (UIS) on ULCD is positive in eastern cities (0.038) but opposite in central or western cities (−0.024). Further results show that the positive effects of optimization of industrial structure (OIS) and UIS are gradually increasing with the improvement of ER. However, the positive effects are more beneficial to the eastern cities. Therefore, the conclusions of this study can provide a decision-making reference for local government to comprehensively formulate environmental and industrial policies to enhance the low-carbon development of cities.

Urban Low-Carbon Consumption Performance Assessment: A Case Study of Yangtze River Delta Cities, China

Urban low carbonization has been an essential element in China’s carbon peak and carbon neutrality strategies. An assessment of urban low-carbon performance could provide valuable information for monitoring and guiding the low-carbon transition in cities. However, due to cross-regional carbon transfer, the actual level of achievement would be masked, if the assessment was based only on a production-based index such as carbon emission intensity (CEI). Focusing, instead, on consumption-based low-carbon performance, this study calculated levels of urban carbon consumption intensity (CCI) based on city-level carbon footprint accounting, investigated the patterns and drivers of changes in CCI of 26 Yangtze River Delta (YRD) cities from 2012 to 2015, and conducted a comparative analysis of CEI and CCI data from both static and dynamic viewpoints. It was found that the CCI of YRD cities decreased from 1.254 to 1.153 over the period. Cities at higher economic levels were found to have lower CCI values. Decomposition results show that shifts in production structure, intensity of emissions and changing consumption patterns contributed to the decline in CCI of the YRD area. Richer cities were found to show greater declines in CCI due to decarbonizing structures in production and consumption. The comparative results show that although the CEI and CCI of cities were generally correlated in both static level and dynamic change, the net carbon transfer impacted the correlation sensitivity between various cities. Finally, our findings provide practical guidance on achieving coordinated emission reductions at an inter-city level from both production and consumption perspectives.

A data-driven approach to objective evaluation of urban low carbon development performance

An effective evaluation of a city's low carbon development plays an essential role in promoting low carbon development strategies for achieving the city's sustainable development. This paper proposes a data-driven approach to objectively evaluating the low carbon development level of cities. The approach formulates the low carbon development evaluation problem as a multi-criteria decision analysis problem and incorporates the merits of bibliometric analysis, text mining and optimal weighting to evaluating the urban low carbon development performance. The bibliometric analysis is applied to systematically identify evaluation criteria and associated indicators and establish an evaluation system for measuring low carbon development levels of urban cities. Equipped with an objective weighting method based on text mining, the approach determines the local weights of the evaluation criteria and indicators for each city by extracting subjective preferential information from the policy documents available on the local government's websites. Two optimal weighting models are developed to determine the optimal global weights of the indicators and criteria by maximizing the low carbon development performance of all cities. The obtained criteria weighting thus can reflect both the preferences of local city governments and the best common interest of all cities involved in the evaluation. The approach is then illustrated with a case study on three cities involved in urban agglomeration planning in China. The results compare the low carbon development performance of the cities, identify the disparities between the cities and reveal each city's obstacle factors that hinder its development. Policy recommendations are then suggested for developing effective low carbon development policies.

The Coupling Relationship Between Urbanization and Carbon Emissions From Land Use in Ningxia

To understand the complex relationship between urbanization and carbon emissions, this study measures the urbanization level and land-use carbon emissions of 19 counties and districts in Ningxia from 2009 to 2019 using the integrated index method and carbon emission model, respectively, and explores the coupling relationship based on the coupling coordination degree model, to provide theoretical support for low-carbon urbanization and carbon emission reduction planning in Ningxia. The research results showed that the comprehensive evaluation index of the Ningxia urbanization system shows a linear increasing trend with an average annual growth of 1.6%, but the overall level is low, and the overall spatial pattern being high in the North and low in the South is shown. The dominant type of urbanization is population and economic urbanization. The average annual growth rate of net carbon emissions from land use in Ningxia is 4.75 106t/a. Spatially, it is characterized by higher levels in cities in the North along the Yellow River Ecological and Economic Zone and lower levels in the central arid zone and southern mountainous areas. The coupling degree and coupling coordination degree of Ningxia urbanization and land use carbon emissions show a fluctuating upward trend but belong to a low level of mutual constraints, i.e., the low level of development of Ningxia urbanization, backward industries, and low energy utilization lead to the growth of carbon emissions in Ningxia and restrict the development of urbanization. The government should take into account the regional differences when formulating carbon emission reduction policies to maintain appropriate population size, optimize the economic structure, and improve the land use efficiency.

The making of low-carbon urbanism: Climate change, discursive strategy, and rhetorical decarbonization in Chinese cities

Existing studies of the incorporation of climate mitigation into urban governance are concerned overwhelmingly with the material aspects of low-carbon urbanism. Emphasis is placed upon the measurable, quantifiable, and technical characteristics of carbon, whereas the (uneven) leverage of its rhetoric value across different state spaces/scales is largely overlooked. This paper engages with ongoing theoretical enquiries through a study that applies a discursive-analytical perspective to examining the rise of low-carbon cities (LCCs) and the actual practicing of rhetorical decarbonization strategies in Shenzhen, China. The making of the LCC is found to be incentivized by the perceived symbolic value and enabling power of the climate agenda in attracting higher-level government supports, maintaining local collations, and enticing international attention and investment. This enabling power depends on the interpretive flexibility within decarbonization discourse and the manipulation of the discourse to justify involuntary socio-economic changes. Findings of this research call for greater attention paid to the overlooked discursive dimensions of low-carbon urbanism as the constitutive elements of ongoing global urban transformation processes.

Measuring Urban Low-Carbon Sustainability in Four Chinese Cities

Practices and research on measuring traditionally urban sustainability abound, therefore the challenge now is related to how the urban carbon issues are included into current measuring methods, thus there is a need to develop methods for measuring urban low-carbon sustainability. In this paper, a simple method, which is based on low-carbon sustainability index, is developed. The overall urban low-carbon sustainability index is the weighted sum of 11 single indices, and each single index is defined as the indicator assessing the development level against the baseline. The baseline is often the criteria or the minimum requirement of low-carbon sustainability. Case studies in four Chinese cities have put this method into practice, and the results show that all four selected cities fail to pass the testing of sensible low-carbon sustainability rule and they are all in weakly low-carbon sustainable development. Although the four cities have made great progress in their capacity building on pollution control and their capacities on wastewater treatment, main pollutants’ removal and household and hazardous wastes treatment are enough to meet the needs of local development, they are all facing the great challenges on using of sustainable energy, offsetting of CO2 emissions and adoptions of nature-based solutions. The method developed by this research is a useful tool for decision makers identifying whether the local development is not on a low-carbon sustainable path.

The Impact of Outward Foreign Direct Investment on Carbon Emission toward China’s Sustainable Development

It is of practical significance to evaluate whether enhancing outward foreign direct investment (OFDI) can promote carbon mitigation under the new urbanization background toward China’s sustainable development and carbon neutrality. The impact of OFDI on carbon emissions is investigated from the dual perspectives of the urbanization threshold and the mediating path by using panel data from China’s 30 provinces during the period of 2003–2015 and considering both population and land. The results show that there is a significant impact from interprovincial OFDI on CO2 emissions with the double threshold effect of urbanization, and that OFDI expansion will increase CO2 emissions with urbanization; however, the different stages of urbanization show inverted U-shaped characteristics that first rise and then fall. The optimization of industrial structures has not passed the mediating effects test during the sample period, while the rationalization of the industrial structure provides a mediating effect in the primary stage of urbanization and a suppressing effect in the high level stage of urbanization. Import dependence only shows a masking effect in the intermediate stage of urbanization, while the technical level shows an intermediary effect in the primary stage of urbanization and a masking effect in the intermediate stage. The intensification of OFDI has brought different effects on economic and social production in various regions of China under urbanization, which has further affected regional carbon emissions. Discussing these effects would help to provide constructive suggestions for the regional coordination of development, new urbanization construction and urban low carbon transformation.

The impact of land urbanization on carbon dioxide emissions in the Yangtze River Delta, China: A multiscale perspective

Urban areas have become one of the main sources of CO2 emissions. Considering the spatial interactions between county scale and prefectural scale divisions and spatial spillover effects, this study employed a hierarchical spatial autoregressive model to investigate the impact of land urbanization on CO2 emissions in the Yangtze River Delta (YRD) during 1995–2015. The results showed that CO2 emissions exhibited spatial clustering across the counties and prefectural cities in the YRD region. The prefectural cities with the High-High type were mainly concentrated in Shanghai, Suzhou and Nantong during 1995–2015, indicating that they were the main areas that required CO2 emissions reduction. Land urbanization has a positive effect on CO2 emissions; there is an inverse U-shaped relationship between land urbanization and CO2 emissions. CO2 emissions presented spatiotemporal variations between the county and prefectural scales in the YRD region. The spatial interactions between county scale and prefectural scale suggest that local governments should integrate their neighboring governments (higher and lower governments) to reduce CO2 emissions in the YRD. The population, GDP per capita, carbon intensity, and spatial factor also played a significant role in reducing CO2 emissions. The results provide useful information for low-carbon urbanization development in the YRD.