Smart City Construction Research Articles

Smart city and green innovation: Mechanisms and industrial emission reduction effect

Green innovation is essential for environmentally sustainable development. The construction of smart cities offers significant potential for developing green innovation through optimizing urban administration and improving the allocation of critical resources. Using Chinese city data from 2005 to 2019, this study adopts a causal identification framework based on the multi-temporal difference-in-difference method to explore the impact of smart city construction on green innovation and the mechanism and joint industrial emission reduction effect between them. A positive and significant relationship with a weak inverted U-shaped trend was found between smart city construction and green innovation. Besides the direct channel, labor factor allocation, venture capital attractiveness, and market accessibility are essential indirect channels between the two concepts. Furthermore, the effects of smart city construction on green innovation varied depending on the marketization level, administrative rank, population size, and geographic location of the city. In addition, the interaction of the two constructs negatively affected industrial emissions, which helped optimize the environment. These findings suggest that smart city construction offers a digital dividend for developing green innovation and creating an efficient, sustainable environment.

The impact of smart city construction on urban energy efficiency: evidence from China

The impact of smart city construction on urban energy efficiency: evidence from China

Application of Wireless Network Remote Sensing Image and Video Processing Technology in Smart City Design and planning

With the rapid development of society, regional urban construction has become the main guarantee of economic development, and smart city construction has become the main trend of regional development. Smart cities consist of information images and urban development, which can meet the needs of the modern generation for material life, knowledge and scientific life. Therefore, wireless remote sensing (RS) and video image processing have become key tools for the development of smart cities, which can solve the problems encountered by many professionals before, and become an integral part of urban construction. On this basis, this paper summarized the video processing image and wireless network RS image, and analyzed their development and application in urban design and layout. Based on this, this paper expounded the application of wireless network RS image in smart city design and layout, and analyzed the application prospects of RS technology in building smart city space-time information framework, RS+urban management, RS+smart environmental protection, RS+smart city agriculture and video processing technology in smart city design and planning. It analyzed the application in intelligent security, intelligent transportation and management visualization, and then used the two-dimensional discrete Fourier transform (2D-DFT) definition to strengthen the design and layout of smart cities. According to experiments and surveys, introducing 2D-DFT into the design and layout of smart cities can build a more intelligent city and improve residents’ satisfaction by 30%.

On the Information Construction of Smart City from the Perspective of Urban Planning

With the acceleration of global urbanization and the development of science and technology, smart cities have attracted much attention. The information construction of smart cities plays an important role in urban planning. This paper discusses the importance of information construction in smart cities, and emphasizes its role in promoting urban development and optimizing resource allocation. Key factors and challenges include data security and technical standards. Suggestions on strengthening policy guidance, promoting technological innovation and strengthening cooperation mechanism are put forward. Ultimately, the purpose is to provide guidelines for the future urban planning of smart city information construction and promote the sustainable development of the city.

A Design of Model Compression Method Based on YOLOv5

In response to the requirements for detection accuracy and speed in security detection technology in current smart city construction, a model compression method based on YOLOv5s is proposed, aiming to lighten the original model and detect the opening and closing states of car windows. Firstly, a structured pruning method based on channel weights is employed to perform channel pruning. Then, a fine-tuning training method based on logical distillation is adopted to restore the network detection accuracy. Experimental results on the car window dataset show that compared with the original algorithm, the improved algorithm only loses 0.3% in detection accuracy while reducing the number of parameters and computations to 41.5% and 44.3% of the original model, respectively. This significantly reduces the model complexity and achieves model lightweighting.

Impact Analysis of Regional Smart Development on the Risk of Poverty among the Elderly

As China continues to introduce policies to promote the construction of smart cities, the governance capacity and living environment of many pilot regions have moved towards smart development and sustainability. In order to reveal the impact of improving regional smart development on the lives of the elderly, we explored the relationship between regional smart development and the risk of poverty in old age. The results show that at present, the development of smart cities continues to grow; the majority of elderly respondents’ poverty risk is general; the development of smart cities at the regional level is effective in reducing the poverty risk of the elderly in the region, with the degree of impact varying by region; and the impact of smart development at the regional level on the risk of poverty in old age varies with different levels of social support. Based on this, suggestions are made to vigorously develop the regional economy, improve the social security mechanism for the elderly, and accelerate the digitalization and humanization of infrastructure so as to better meet the needs of China’s elderly population in the context of high-quality smart development in the region and achieve sustainable development goals.

Smart city and earnings management: Evidence from China.

Smart cities improve services for businesses, among many other benefits. A comprehensive understanding and effective utilization of these advantages is crucial for promoting business development. Using panel data from Chinese listed companies (2010-2020), this study employs a multi-stage DiD model to investigate the impact of smart cities on corporate earnings management. The findings indicate that the smart city pilot policy has significantly reduced corporate earnings management. Further analysis suggests that smart cities primarily reduce earnings management by improving firms' external information environments. Additionally, the results show that the policy impact of smart cities is more significant in regions with lower regulatory intensity or higher marketization levels, compared to regions with higher regulatory intensity or lower marketization levels. Similarly, firms in less concentrated markets or those more closely related to smart city development tend to experience greater reductions in earnings management due to smart city construction, unlike firms in more concentrated markets or those less involved. Finally, this paper offers several brief suggestions.

Paying Attention to Vehicles: A Systematic Review on Transformer-Based Vehicle Re-Identification

Vehicle re-identification (v-reID) is a crucial and challenging task in the intelligent transportation systems (ITS). While vehicle re-identification plays a role in analysing traffic behaviour, criminal investigation, or automatic toll collection, it is also a key component for the construction of smart cities. With the recent introduction of transformer models and their rapid development in computer vision, vehicle re-identification has also made significant progress in performance and development over 2021-2023. This bite-sized review is the first to summarize existing works in vehicle re-identification using pure transformer models and examine their capabilities. We introduce the various applications and challenges, different datasets, evaluation strategies and loss functions in v-reID. A comparison between existing state-of-the-art methods based on different research areas is then provided. Finally, we discuss possible future research directions and provide a checklist on how to implement a v-reID model. This checklist is useful for an interested researcher or practitioner who is starting their work in this field, and also for anyone who seeks an insight into how to implement an AI model in computer vision using v-reID.

Investigation of Artificial Intelligence Algorithms in Robot Object Recognition Systems Under the Background of Big Data

In the long history of human beings, with the continuous exploration and research of natural phenomena and social life, many scientific fields have emerged, and robots are the product of this technological development to a certain stage. At present, there are hundreds of different types of robots applied in production and daily life in the world, which have achieved significant economic benefits. However, its technical issues have gradually emerged. For example, the shortcomings in visual perception and other aspects cannot be effectively addressed. Object recognition is not precise enough, and information resources cannot be effectively utilized to achieve control functions. These are the main factors that constrain the further progress and improvement of robots. The emergence of big data and Artificial Intelligence (AI) has brought unprecedented opportunities to robots. Especially, the application of big data analysis in intelligent manufacturing and smart city construction is becoming increasingly widespread, thus providing new solutions for robot services. They not only enable people to quickly and accurately grasp a large amount of valuable knowledge, but also better tap into the enormous potential contained in human intelligence, which largely drives the robot industry towards intelligence. By summarizing the existing research results, this paper explored the development trend of robot object recognition systems, and focused on its key technologies, the feature matching-based pattern recognition and acceleration strategy-based detection efficiency improvement. In response to the current problems, corresponding solutions were proposed and comparative experiments were designed. This proved that the anti-interference detection accuracy of the robot object recognition system based on big data and AI algorithm improved by about 12.48%, thus hoping to provide reference for future robot system development.

The impact of smart city construction (SCC) on pollution emissions (PE): evidence from China

Based on panel data from 210 prefecture-level cities in China from 2003 to 2021, this study employs the Time-Varying Differences-in-Differences (Time-Varying DID) approach to systematically examine the impact of smart city construction on pollution emissions and its underlying mechanisms. Additionally, the Propensity Score Matching–Differences-in-Differences method is employed for further validation. The research findings indicate that Smart City Construction (SCC) significantly reduces urban Volume of Sewage Discharge (VSD), sulfur dioxide emissions (SO2), and Emissions of Fumes and Dust (EFD), thereby mitigating pollution emissions (PE) and enhancing environmental quality. Mechanism analysis reveals that SCC achieves these effects through scale effects, structural effects, and technological effects. City heterogeneity analysis shows that provincial capital cities exhibit a stronger suppression effect on pollution emissions compared to non-provincial capital cities. Moreover, cities with lower levels of education attainment demonstrate a stronger ability to curb pollution emissions, while larger cities exhibit a more pronounced impact on mitigating pollution emissions. The marginal contributions of this study mainly consist of three aspects: Firstly, it enriches the literature on environmental impact factors by assessing, for the first time, the influence of SCC on PE. Secondly, a comprehensive approach is employed, integrating VSD, EFD, SO2 data, and economic and pollution data at the city level. Time-Varying DID is used to evaluate the policy effects of SCC. Finally, the study analyzes the impact mechanisms of SCC policy on environmental emissions from various perspectives.

CUS3D: A New Comprehensive Urban-Scale Semantic-Segmentation 3D Benchmark Dataset

With the continuous advancement of the construction of smart cities, the availability of large-scale and semantically enriched datasets is essential for enhancing the machine’s ability to understand urban scenes. Mesh data have a distinct advantage over point cloud data for large-scale scenes, as they can provide inherent geometric topology information and consume less memory space. However, existing publicly available large-scale scene mesh datasets are limited in scale and semantic richness and do not cover a wide range of urban semantic information. The development of 3D semantic segmentation algorithms depends on the availability of datasets. Moreover, existing large-scale 3D datasets lack various types of official annotation data, which hinders the widespread applicability of benchmark applications and may cause label errors during data conversion. To address these issues, we present a comprehensive urban-scale semantic segmentation benchmark dataset. It is suitable for various research pursuits on semantic segmentation methodologies. This dataset contains finely annotated point cloud and mesh data types for 3D, as well as high-resolution original 2D images with detailed 2D semantic annotations. It is constructed from a 3D reconstruction of 10,840 UVA aerial images and spans a vast area of approximately 2.85 square kilometers that covers both urban and rural scenes. The dataset is composed of 152,298,756 3D points and 289,404,088 triangles. Each 3D point, triangular mesh, and the original 2D image in the dataset are carefully labeled with one of the ten semantic categories. Six typical 3D semantic segmentation methods were compared on the CUS3D dataset, with KPConv demonstrating the highest overall performance. The mIoU is 59.72%, OA is 89.42%, and mAcc is 97.88%. Furthermore, the experimental results on the impact of color information on semantic segmentation suggest that incorporating both coordinate and color features can enhance the performance of semantic segmentation. The current limitations of the CUS3D dataset, particularly in class imbalance, will be the primary target for future dataset enhancements.

Promoting Urban Innovation through Smart Cities: Evidence from a Quasi-Natural Experiment in China

It is of great theoretical and practical significance to investigate the influence of intelligent city construction on urban innovation. Based on the data of 238 cities in China from 2006 to 2019, this paper utilizes the staggered difference-in-differences (staggered DID) model and the mediating effect model to examine the impact and mechanisms of smart city construction on urban innovation. We find that China’s smart city pilot policies significantly promote urban innovation. Mechanism analysis shows that this innovation promotion effect acts through improving urban informatization, government financial expenditure on science and technology, and the upgrading of the city’s industrial structure. Further analysis shows that smart city construction has a stronger promoting effect on innovation in cities of a larger scale, that located in the eastern region, and have a lower level of science and education. Overall, our findings provide new insights into urban innovation and objectively assess the impact of smart city construction in China.

Analysis on the impact of smart city construction on urban greenness in China's megacities

Urban greenness serves as a key indicator of sustainable urban development, with smart city construction emerging as a primary strategy for its enhancement. However, there is little empirical evidence considering multi-dimension between urban greenness and smart city construction on the city level. This study focuses on the impact on urban greenness of smart city construction in megacities, using the difference-in-differences regression model to evaluate the impact based on urban development conditions in various aspects from 2010 to 2021 in 10 megacities in China. The results of panel data of different indicator samples show unique conclusions. First, smart city pilot policy in megacities has significant impact on urban greenness, primarily due to demographic and economic developments. Second, the impact is different between the megacity and national level, and different factors of urban greenness have different effects on smart city construction. Third, the effects are time-lagged and lasted for years, and regional heterogeneity divided by building climate zones is existed, where the effect is more obvious in city agglomeration. These findings of smart city construction reveal the unique influences on megacity greenness, and can be generalized to cities with similar characteristics accordingly.

Focus on digital construction of smart cities from COVID-19 epidemic prevention and control.

Focus on digital construction of smart cities from COVID-19 epidemic prevention and control.

Image Enhancement Research System Based on OpenCV

As a carrier of information, image analysis and processing are indispensable in the construction of smart cities. For example, object detection, tracking and recognition in the study of automatic driving all rely on external input images. Therefore, high-quality images play an important role in the era of intelligence, and the study of image enhancement has very high practical significance. In this paper, the convolutional neural network is designed for feature extraction to achieve fuzzy image enhancement, and the image enhancement research system is designed based on openCV and Qt.

Research on the Application of Digital Twins Technology in the R and D Stage of Automobiles Enterprises

As a pillar industry of the country, the digital transformation of the automobile industry is of great significance to optimize the upgrading of the automobile industry structure, promote the long-term development of automobile enterprises, and accelerate the construction of smart cities and smart transportation. However, for all automobile enterprises to promote the digital transformation of automobile industry, it is necessary to formulate a complete set of digital transformation plan for the whole life cycle of automobile product development, among which Digital twins technology can be used as the first choice of digital transformation plan. Only by promoting digital transformation can enterprises be competitive in the future development.

Research on Antecedent Configuration of Smart City Governance Path: Qualitative Comparative Analysis of Fuzzy Sets Based on 30 Cities in China

This study focuses on 30 typical cases of smart cities in China, constructing an analytical framework that includes seven explanatory factors: information infrastructure, administrative pressure, government attention, public service level, financial support, technical talent, and government-enterprise cooperation. Utilizing the fuzzy set qualitative comparative analysis method, the study conducts configuration analysis of the influencing factors for the efficient construction of smart cities. The research reveals that government-enterprise cooperation and the public service level are crucial factors for achieving high construction efficiency in smart cities. These are collectively categorized into three construction models: "service innovation type driven by pressure," "multi-factor-induced type driven by technology," and "proactive follow-up type under fiscal incentives." The results of the study provide reference points for the construction of smart cities in different regions of China. Urban governments should undertake strategic and combinational construction actions based on their unique characteristics and resource endowments.

A green path towards sustainable development: The impact of carbon emissions trading system on urban green transformation development

This study aims to explore the direct and indirect impacts of the Carbon Emission Trading System (CETS) on the green transition development (GTD) of Chinese cities. This paper constructs a GTD evaluation system from the perspective of prevention and governance at the source, in terms of carbon reduction, pollution reduction, green expansion and growth, and measures the GTD performance of 281 cities based on the EW-TOPSIS model. It is found that the average increase in GTD of the sample cities from 2006 to 2020 is 3.40 %, the GTD progress of the 281 cities has a considerable positive spatial autocorrelation, with the GTD progress of the CETS pilot cities being significantly bigger than that of the non-pilot cities. Further this paper adopts the spatial difference model (S-DID) to scientifically identify the influence mechanism and heterogeneity of the direct and spatial effects of CETS on city GTD, and the results show that CETS has a positive effect on the GTD of both local and neighboring cities. Propensity score matching (PSM) is used to further corroborate these findings. The study also shows that CETS improves local GTD by promoting green innovation, rationalizing industrial structures, and enhancing energy efficiency in cities. Nevertheless, the ensuing siphon effect hinders nearby cities' levels of energy efficiency, industrial structure rationalization, and green innovation. The policy effects of CETS are more significant in large cities, eastern cities, old industrial areas, and non-resource-based cities. CETS can work in synergy with smart city construction, low-carbon city pilot policies, and emission trading policies to accelerate the process of GTD.

Smart Cities and Greener Futures: Evidence from a Quasi-Natural Experiment in China’s Smart City Construction

As the digital economy becomes the new engine of economic growth, China has introduced a series of smart city policies aimed at promoting high-quality and sustainable urban development. This paper aims to evaluate the green development effects of China’s “Smart City Pilot” policy and to explore the heterogeneity of policy effects across different types of cities. Using panel data from 283 prefecture-level cities in China from 2006 to 2020, this study examines the relationship between smart city construction policy and urban green development efficiency using the green total factor productivity (GTFP). We employ the Causal Forest and mediation effect models to estimate the impact of smart city pilot policy on GTFP and explore the underlying mechanisms. The main results are: (1) The smart city pilot policy significantly enhances urban GTFP, a finding consistent across diverse policy evaluation approaches. (2) The influence of the policy on green development varies among cities, and such heterogeneity is effectively captured by the Causal Forest. (3) This varied impact primarily stems from urban location factors and inherent characteristics. Notably, the policy effect in Eastern China outpaces that in other regions. The policy yields greater green benefits with financial development and medical capital rises, but excessive government public expenditure curtails its positive influence. (4) The mediation mechanisms through which the smart city pilot policy promotes green development exhibit certain differences between the “high-effect group” and the “low-effect group”. The former predominantly leverages innovation-driven and agglomeration effects, while the latter chiefly relies on industrial structural advancement and rationalization.

Can the Process of Evaluation and Recognition Effectively Promote Water Conservation in Cities? Evidence from China

Water-saving construction is a crucial technique for China to address water resource scarcity and tackle the water-use issues. Can evaluation and recognition result in urban water-saving construction, and can it produce a more effective water-saving effect with the help of smart city construction? Based on the unbalanced panel data of prefecture-level cities in China from 2006 to 2019, this paper empirically discusses the impact of China’s National Water-saving City Selection on urban water conservation by using the multi-time point difference-in-differences method, and on this basis, it includes the consideration of smart city construction, so as to investigate the strengthening role of urban digital management in urban water-saving construction. The findings show the following: (1) the National Water-saving City Selection has significantly improved water conservation in the evaluated cities, and it has contributed to the positive effect via direct and indirect channels. (2) The water-saving effect generated by the policy varies with the total urban water supply or the number of people with a higher education in the city. (3) The implementation of the smart city pilot policy has significantly strengthened the city’s existing water-saving construction, and a further mechanism analysis shows that it not only strengthens the existing direct impact channels but also compensates for past construction shortcomings.