
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12312-2
10.1016/j.heliyon.2024.e36281
e36281
Research Article
Are cities under bright lights more innovative? Evidence from China
Jiang Zhenyu jzyazy@jiangnan.edu.cn
a
Li Zhubo lizhubo@whut.edu.cn
b⁎
Wang Jianhua jianhua.w@jiangnan.edu.cn
a
a School of Business, Jiangnan University, No. 1800, Lihu Avenue, Binhu District, Wuxi, Jiangsu Province, China
b School of Entrepreneurship, Wuhan University of Technology, No. 122 Luoshi Road, Hongshan District, Wuhan, Hubei Province, China
⁎ Corresponding author. Zhubo Li, School of Entrepreneurship, Wuhan University of Technology, No. 122 Luoshi Road, Hongshan District, Wuhan, Hubei Province, China. lizhubo@whut.edu.cn
13 8 2024
30 8 2024
13 8 2024
10 16 e3628116 6 2023
7 8 2024
13 8 2024
© 2024 The Authors. Published by Elsevier Ltd.
2024

https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
The prosperity of the nighttime economy is an important criterion for measuring urban development. Based on regression analysis of data from 330 prefecture-level cities in China, this study explores the potential link between urban lighting and urban innovation capacity. The results show that the relationship between nighttime light intensity and urban innovation capacity follows an inverted U-shaped curve. Moreover, urban nighttime light stability moderates the above relationship. In addition, the heterogeneity analysis indicates that low population density, weak economic foundation, and high administrative grade weaken the relationship between lighting intensity and urban innovation capacity. Finally, the mechanism analysis suggested that the inverted U-shaped relationship between urban lighting and innovation capacity may be caused by the encroachment of the service industry and the loss of urban attractiveness. By exploring the proxy effect of nighttime lighting on urban innovation, this study reveals the imbalance between extrinsic image and the intrinsic capacity of cities, which not only extends the research on urban construction and development but also provides guidance for local governments concerning how to achieve all-round coordination of cities.

Highlights

• There is a proxy effect of nighttime lighting on urban innovation capacity.

• Urban extrinsic lighting is mismatched with their intrinsic innovation capacity.

• Industrial imbalance and environmental pollution may be the causes of this mismatch.

• The relation of lighting intensity and urban innovation varies with urban traits.

• The results contribute to the research and practice on balanced development of cities.

Keywords

Nighttime light intensity
Nighttime light stability
Urban innovation capacity
Chinese cities
==== Body
pmc1 Introduction

In the era of the knowledge economy, innovation is the primary power source for development [1]. As the core carrier of innovation and entrepreneurship activities, cities have undoubtedly become gathering places for various resources and talent [2,3]. Wang et al. (2021) highlighted that the key to building an innovative country lies in the enhancement of urban innovation capacity [4]. However, due to differences in regional conditions, the innovation capacity among cities exhibits significant heterogeneity. For example, according to the National Bureau of Statistics of China, eastern cities are significantly greater than western cities in terms of net population inflow, R&D expenses, and patent applications [5]. Similar situations also exist in many other countries. To overcome this imbalance, a number of cities have competed to carry out all-around image projects to improve urban attractiveness and innovation vitality [6]. Under this background, the nighttime lighting level and aesthetics have gradually become important indicators for measuring a city's appearance. Through a data survey of prefecture-level cities, Wang et al. (2022) reported that the proportion of urban investment in lighting and related facilities has increased annually, and this phenomenon is more common in the central and western regions [7]. The research of Wicht and Kuffer (2019), which studied European cities, came to a similar conclusion [8].

When studying urban construction, it can be analyzed from the extrinsic and intrinsic dimensions [9]. Extrinsic construction mainly involves infrastructure allocation, layout planning, and lighting, which can highlight the charm and ability of a city [10,11]. Intrinsic construction includes the cultivation of urban management ability, innovation capacity, and comprehensive competitiveness, which directly affects urban development [12]. As a core element of urban extrinsic construction, the intensity of urban lighting not only is an important measure of the appearance of a city but also reflects the living conditions of residents and a city's talent attractiveness [13]. Hollands and Chatterton (2003) suggested that nightlife is an important way for urban people to pursue spiritual satisfaction, and the intensity of nighttime lights reflects the level of urban people's nightlife [14]. Amaral et al. (2006) found that cities with higher population growth rates present stronger lighting intensity, which further indicated that people consider the richness of the city's nightlife when choosing where to live [15]. According to Jiang et al. (2020) and Filippetti and Zinilli (2023), talent agglomeration is a necessary condition for regional innovation [16,17]. Based on the above logic, some studies have examined the proxy effect of urban light intensity on innovation from a theoretical perspective [4,18]. Unfortunately, the existing research does not use urban nighttime light data as an important reference when examining urban innovation vitality and capability, and few studies have explored the relationship between the two in depth from an empirical perspective [19,20]. Therefore, do cities under bright lights truly have strong innovation capacity? To better answer the question, this study explores the direct relationship between urban light intensity and innovation capacity and further analyze the moderating effect of urban light stability on the above relationship.

The contributions of this study are as follows. First, by exploring the proxy effect of urban lighting on urban innovation capacity, this study can deepen the academic understanding of the relationship between urban extrinsic construction and intrinsic construction. Currently, most studies measure urban innovation capacity based on patent data and R&D investment [2,21]. Although these indicators can accurately reflect the basic situation of urban innovation, they lack intuitiveness. By examining the relationship between nighttime light intensity and urban innovation capacity, this study not only reveals the proxy effect of urban lighting data on urban innovation but also links the extrinsic and intrinsic construction of a city, which can provide an important reference for related theoretical research on urban management. Second, this study reveals the imbalance between urban extrinsic lighting construction and intrinsic innovation capacity, providing a new direction for research on the overall coordinated development of cities and regions. More specifically, the heterogeneity of the above relationships from the perspectives of geographic location, population density, economic level, and administrative grade are examined and the reasons for the inverted U-shaped relationship between the two from the aspects of industrial structure and environmental quality are further analyzed. Notably, many cities have fallen into a dilemma in the process of development and are unable to effectively balance extrinsic and intrinsic construction [10,22]. Although the development of cities and regions depends on the attraction of extrinsic construction, it fundamentally needs to be driven by intrinsic construction.

2 Theory and hypotheses

2.1 Construction and development of cities

With the progress of the economy and society, urban construction is also presenting a trend of diversification. Several cities rely on a superior natural environment to carry out tourism construction; some cities may vigorously promote the construction of railway transportation by virtue of their geographical advantages [23], while other cities may focus on frontier industries and actively build innovative ecological clusters [24]. Regardless of what kind of construction is being carried out, it can be classified into two categories: extrinsic appearance construction and intrinsic capacity construction. Existing research has analyzed the impact of urban intrinsic capacity on urban development from multiple perspectives. For instance, Zhang et al. (2022) discussed the role of openness in promoting high-quality urban development, and Boyer (2015) examined the relationship between urban grassroots innovation and sustainable development [25,26]. In addition, some research has analyzed the effect of intelligent transformation on the overall development of cities [27,28]. In turn, urban development will also promote the construction of urban extrinsic appearance and intrinsic capacity. By comparing the construction of urban infrastructure, Li et al. (2020) found that urban economic growth will promote the construction of rail transit [29]. Additionally, much evidence shows that the level of urban development determines the number of high-rise buildings. With the analysis of urban panel data, Li and Du (2022) confirmed that a suitable development structure and economic agglomeration can accelerate the enhancement of urban innovation capacity [30].

The above review shows that although the construction of an urban extrinsic appearance cannot directly promote urban development, it can become an indicator of urban intrinsic capacity construction and urban development [23]. Unfortunately, previous studies have not delved into the proxy effect of urban extrinsic appearance construction on urban intrinsic capacity construction. Vanolo (2008) noted that the construction of an urban extrinsic appearance involves many aspects, such as road planning, building layout, and lighting [10]. Nighttime lighting is a popular indicator because of its visual impact and data availability [31,32]. Therefore, urban nighttime lighting is selected as a representative of urban extrinsic appearance construction. Li et al. (2020) suggested that urban intrinsic capacity construction mainly includes urban carrying capacity, urban sustainability, and urban innovation capacity [29]. Among these intrinsic capacities, innovation capacity is the fundamental force for promoting the continuous development of cities. Therefore, this study attempts to investigate the proxy effect of urban night lighting on urban innovation capacity.

2.2 Theoretic analysis

The relationship between urban nighttime light and innovation capacity can be analyzed from two theoretical perspectives. According to signal theory, cities, like enterprises, are more willing to show their bright side to win public recognition and praise. Many studies have shown that in regions with better economic development, urban infrastructure and public services are more common [33,34]. This is mainly to highlight the charm and image of the city to improve its attractiveness to potential migrants. As a part of the construction of extrinsic urban appearances, night lighting is an important element for reflecting urban images [35,36]. Several studies have used data from the Defense Meteorological Satellite Program (DMSP) and the National Polar-orbiting Partnership (NPP) of the USA to carry out applied research related to the social economy and the urbanization process, and most of the findings have shown that urban lighting is positively related to economic growth and regional expansion in cities [37,38]. Additionally, some researchers have suggested that there is no simple linear correlation between the urban development level and urban lighting. This is because their relationship may be affected by factors such as urban size and population density [[39], [40], [41]]. Although the existing research has not directly examined the proxy effect of nighttime lighting on urban innovation capacity, it can be inferred that there is a positive correlation between them based on signal theory.

Additionally, in terms of the competition effect, cities are preparing to compare themselves with other cities. To understand the strength of the city, local governments will try to win the competition in various ways. However, due to this competitive effect, many cities pay too much attention to the construction of extrinsic appearances and neglect the cultivation of intrinsic capacity [42]. Several studies have shown that because extrinsic appearance construction is more intuitive, in addition to some important development indicators, such as gross domestic product (GDP), the focus of urban competition is mainly on infrastructure, night lighting, and other observable aspects [43,44]. This can explain, to some extent, why some cities with relatively low development levels have better extrinsic appearances. Based on an investigation of Chinese cities, Chen et al. (2022) proposed that similar to other extrinsic appearance constructions, urban nighttime lighting also presents obvious redundancy and idleness in many Chinese cities [45]. When the city focuses too much on extrinsic lighting construction rather than intrinsic innovation capacity construction, the development of the city will lose balance and show serious inconsistency. Therefore, it can be speculated that the existence of a competition effect may reduce the accuracy of the proxy effect of urban lighting on innovation capacity. Considering that the existing research mainly discusses urban lighting based on the two aspects of light intensity and light stability, this study also tries to investigate the relationship between urban lighting and innovation capacity from these two dimensions and deepens academia's understanding of the proxy effect of nighttime lighting.

2.3 Relationship between nighttime light intensity and urban innovation capacity

From the perspective of signal transmission, nighttime light intensity can well reflect the development status and potential of a city to the outside world and is a key element in the construction of urban images [13,18]. Ceballos et al. (2020)found that cities in the rapid growth stage prefer to carry out publicity by hosting important events and strengthening lighting construction, and these cities often have better economic growth and innovation output [22]. Chen et al. (2020)also noted that cities with high lighting intensity show a relatively high degree of prosperity and technological development. On the other hand, from the perspective of talent agglomeration, bright and beautiful cities are more attractive to innovative talent than less bright and beautiful cities at the same economic level [34]. Lu and Coops (2018) suggested that urban infrastructure and lighting levels are important reference indicators for many new generation talent to choose settlements and that the innovative talent attracted by urban lighting will enhance a city's innovation capabilities [46]. According to the above review, it can be speculated that the intensity of urban light is positively correlated with urban innovation capacity.

However, some research has emphasized that urban lighting is only one of many indicators of urban development and cannot accurately measure urban innovation [37]. Kang and Jung (2019) suggested that moderate urban lighting construction could effectively enhance the urban image and increase the attractiveness of the city, but the pursuit of excessive lighting will negatively affect the growth of the city, which is not conducive to its economic development and technological progress [47]. Through the analysis of regional lighting data, Chen et al. (2022) found that some cities with eye-catching lighting did not experience significant breakthroughs in digital technological progress and innovation output [45]. They speculated that this was because when a city pays too much attention to image projects such as lighting construction, it will reduce its investment in R&D and innovation activities, which is not conducive to increasing its urban innovation capacity. You et al. (2014) noted that urban night lighting could show the vitality of urban development to a certain extent [48]. However, due to competition effects and industry differences, the lighting construction of some cities cannot effectively reflect the local economic level and innovation capacity. Based on the above deduction, cities under bright lights do not necessarily have strong innovation capacity. Accordingly, the following hypothesis is proposed:Hypothesis 1 There is an inverted U-shaped relationship between urban nighttime light intensity and urban innovation capacity.

2.4 Moderating effect of urban nighttime light stability

Nighttime light stability can effectively represent the extent of variation in urban lighting. Based on the perspective of development economics, Gibson et al. (2021) suggested that the change in nighttime lighting is an important indicator of residents' lives [38]. The more stable the regional nighttime lighting, the stronger the regularity of residents' life. Existing research has shown that a regular life can effectively improve the work efficiency and creativity of residents [49], thereby promoting the growth of urban innovation capacity. In addition, a small number of studies have pointed out that stable light data can reflect the stationarity of the social and economic environment in a region to a certain extent [48,50], and these elements are important guarantees for improving urban innovation capacity. From the above point of view, under the same innovation conditions, the greater the stability of urban lighting is, the faster the growth rate of the city's overall innovation capacity. Therefore, this paper speculates that the stability of nighttime light will strengthen the correlation between the light intensity and innovation capacity of a city. That is, with increasing lighting stability, the curve relating the lighting intensity to the urban innovation capacity becomes steeper.

Nighttime light stability is also related to the maturity of urban development. Cities in the rapid growth stage have a significantly greater growth rate of lighting construction, so the stability of nighttime lighting is low. In contrast, in cities that have entered a mature stage of development, the scale of their lighting construction has peaked; therefore, the stability of urban lighting is relatively high [13]. From this viewpoint, cities with high lighting stability generally do not invest much money in lighting construction; thus, the negative effect of lighting intensity on urban innovation capacity can be alleviated. On the other hand, some studies have found that the larger the scale of a city is, the smaller the change in nighttime lighting, which is mainly because first-tier cities are better than other cities in terms of guaranteeing power supply facilities and lighting facility quality. According to Ch et al. (2021), with increasing urban size, the proxy effect of lighting intensity on a city's economic level, social development, and technological progress will also become more accurate [31]. This means that when the stability of urban light is high, the extreme point of the quadratic curve between nighttime light intensity and urban innovation capacity will appear later. Accordingly, this paper proposes the following hypothesis:Hypothesis 2 Urban nighttime light stability significantly moderates the relationship between nighttime light intensity and urban innovation capacity. As the nighttime light stability increases, the curve relation between nighttime light intensity and innovation capacity becomes steeper, and the vertex of the curve shifts to the right.

In terms of the theoretical analysis and hypothesis development, the conceptual model of this study is built, as shown in Fig. 1. This conceptual model can enrich the theoretical fields of urban construction and urban management.Fig. 1 Conceptual model of this study.

Fig. 1

3 Research design

3.1 Samples and data

This study selected China's prefecture-level cities as samples. Although China is a developing country, its economic aggregate is high, and its overall urban development is above the world average. In addition, China has a vast territory and a number of cities, and the development levels of different cities show a normal distribution; therefore, the research conclusions can provide guidance for urban construction in other countries.

The collection process of panel data is as follows. First, the nighttime light data were obtained from the National Oceanic and Atmospheric Administration of the USA. The data consists of surface visible light information (near-infrared electromagnetic wave information) captured at night by DMSP (1992–2013) and NPP (2013–present) Earth observation satellites. Referring to the image data integration method applied by Dong et al. (2020), this paper established a one-dimensional quadratic model to correct the DMSP data and finally obtained consistent nighttime light values in various cities in China [51]. The urban lighting sample is shown in Fig. 2. It can be seen from the figure that China's night lights show a significant distribution of “strong in the east and weak in the west”.Fig. 2 The sample image of Chinese urban lighting.

Note: This figure is drawn based on the data from USA's National Oceanic and Atmospheric Administration.

Fig. 2

Second, this paper obtained urban patent grant data and other related data (including urban GDP, population density, R&D investment, foreign direct investment, etc.) from the Chinese Research Data Services (CNRDS). After excluding data with serious missing data or obvious errors, the panel data from 330 prefecture-level cities in China from 2004 to 2019 were acquired.

3.2 Variable measures

3.2.1 Explained variable: urban innovation capacity

Existing research often selects the number of patent applications or patent grants in a city as a measurement of innovation capacity [2,21]. Although patent applications can more comprehensively reflect the output of innovation, due to the high elimination rate in the review process, they may not be able to accurately measure innovation capacity. To ensure the accuracy of the proxy effect of urban light data, this study uses the “annual number of urban patent grants” to measure the innovation capacity of the target city.

3.2.2 Explanatory variable: urban nighttime light intensity

At present, the most widely used lighting intensity data are DMSP (1992–2013) and NPP (2013–now) data. This study referred to the method of Dong et al. (2020) and measured the urban nighttime light intensity by processing and calibrating the above data and calculating the average value per unit area [51].

3.2.3 Moderating variable: urban nighttime light stability

Currently, the academic community has not established a consistent standard for measuring this indicator. This paper mainly used the mainstream approach to measure the stability of urban lighting by calculating the variance in nighttime lighting per unit area.

3.2.4 Control variables

To exclude the influence of other potential factors and improve the scientificity and credibility of the research, this study drew on the existing research and introduced the following control variables: (1) R&D investment, measured by the proportion of the R&D expenditure in the local public financial expenditure to the regional GDP [34], (2) FDI, measured by the actual amount of foreign investment [7], (3) the educational situation, measured by the number of full-time teachers in ordinary colleges and universities per 10,000 people [21], (4) the scale of government expenditure, measured by the ratio of local public financial expenditure to regional GDP [34], (5) transportation infrastructure, measured by the number of taxis and the number of buses and trams in operation at the end of the year [7], (6) gross regional product [2], (7) green areas [51], (8) and the population situation, measured by the total population and the natural population growth rate of each city [21].

3.3 Model settings

The explained variable in this study is a count variable, which generally requires Poisson regression or negative binomial regression. However, the number of patent grants in Chinese cities varies greatly and does not meet the distribution requirements of negative binomial regression. Therefore, this study performed logarithmic processing on the explained variable to alleviate overdispersion and then used a multidimensional fixed-effects model for regression. In addition, considering that the innovation output of enterprises has a certain lag and that there is the possibility of autocorrelation, to improve the accuracy of model fitting and avoid potential endogeneity problems, regression analysis was carried out using the explained variable with a two-period lag. Referring to Haans et al. (2016) and Chen et al. (2022), the main effect regression model in this paper is as follows [45,52]:(1) Patentit+2=α1+β1UNLIit+β2UNLIit2+β3Controlit+∑City+∑Year+εit

The regression model of the moderating effect of urban nighttime light stability is as follows:Patentit+2=α1+β1UNLIit+β2UNLIit2+β3UNLSit+β4UNLIit*UNLSit

(2) +β5UNLIit2*UNLSit+β6Controlit+∑City+∑Year+εit

where Patentit+2 represents the number of patent grants, UNLIit represents the urban nighttime light intensity, UNLSit is the urban nighttime light stability, Controlit represents the total effect of control variables, ∑City and ∑Year represent city and time-fixed effects, respectively, α1 is the constant term, and εit is the error term.

4 Analysis and results

4.1 Basic descriptive analysis

The descriptive statistics and correlation coefficients of the variables are shown in Table 1, Table 2, respectively. According to Table 1, the average ratio of R&D investment to urban GDP is less than 0.2 %, and the median is only 0.141 %, which shows that Chinese local governments should pay more attention to R&D and innovation. For the urban light data, for both intensity and stability, the standard error is greater than the mean, which indicates that there may be overdispersion in the nighttime light data of Chinese cities.Table 1 Descriptive statistics of main variables.

Table 1Symbols	Variable definition	N	Mean	SD	Median	Min	Max	
Patent	The number of patent grants	4136	0.264	0.821	0.036	0.000	13.974	
RDI	R&D expenditure to GDP	4136	0.199	0.235	0.141	0.000	6.310	
FDI	Foreign direct investment	4136	7.425	17.823	1.796	0.000	308.256	
EDU	The number of full-time teachers in ordinary colleges and universities	4136	0.486	0.946	0.168	0.003	7.049	
GE	The ratio of local public financial expenditure to the regional GDP	4136	0.177	0.131	0.148	0.040	2.349	
TI1	The number of taxis	4136	0.537	4.398	0.145	0.005	93.233	
TI2	The number of buses	4136	0.229	1.831	0.052	0.001	47.580	
GDP	Gross Regional Product	4136	0.186	0.293	0.100	0.004	3.816	
Green	the actual green area	4136	0.568	1.231	0.267	0.000	16.803	
Population	The total population	4136	0.043	0.031	0.037	0.000	0.342	
Population_G	Natural population growth rate	4136	5.806	5.180	5.460	−16.64	40.780	
UNLI	Urban nighttime light intensity	4136	0.720	2.115	0.168	0.000	38.610	
UNLS	Urban nighttime light stability	4136	2.356	3.387	1.460	0.000	65.081	
Note: SD denotes standard deviation.

Table 2 Correlation coefficient matrix of main variables.

Table 2	1	2	3	4	5	6	7	8	9	10	11	
1. Patent	
2. RDI	0.462*											
3. FDI	0.726*	0.380*										
4. EDU	0.511*	0.251*	0.651*									
5. GE	−0.095*	0.172*	−0.098*	−0.139*								
6. TI1	0.586*	0.269*	0.709*	0.741*	−0.089*							
7. TI2	0.738*	0.388*	0.716*	0.703*	−0.114*	0.689*						
8. GDP	0.673*	0.442*	0.714*	0.713*	−0.147*	0.692*	0.713*					
9. Green	0.523*	0.260*	0.511*	0.562*	−0.127*	0.625*	0.732*	0.665*				
10. Population	0.302*	0.060*	0.395*	0.512*	−0.140*	0.400*	0.377*	0.515*	0.202*			
11. Population_G	0.019	0.080*	−0.058*	−0.042*	0.048*	−0.079*	0.033	−0.023	0.038	0.077*		
12. UNLI	0.664*	0.341*	0.516*	0.286*	−0.157*	0.434*	0.650*	0.588*	0.567*	0.042*	0.068*	
Note: * represents the significance of 0.05.

According to the results in Table 2, the correlation coefficient between light intensity and urban innovation capacity is 0.664, which suggests that innovation capacity is positively related to nighttime light intensity. Furthermore, the variance inflation factor (VIF) of each variable is less than 5. This indicates that there is no significant multicollinearity in this study.

4.2 Hypothesis test

This paper used multidimensional fixed-effects regression to verify the main effect, and the specific results are shown in Table 3. Models (1) and (2) suggest that when the number of urban patent grants is set as an explained variable, the coefficients of urban nighttime light intensity in linear regression and quadratic regression are both significant at 0.01, but quadratic regression has a greater goodness of fit. In addition, since the coefficient of UNLI2 in Model (2) is negative, the relation between urban lighting intensity and innovation capacity follows an inverted U-shaped curve. Therefore, Hypothesis 1 is confirmed.Table 3 Test of the proxy effect of nighttime lighting on urban innovation capacity.

Table 3	(1)	(2)	(3)	
Patent (the number of patent grants)	
RDI	0.267***	0.257***	0.221***	
(0.032)	(0.032)	(0.031)	
FDI	−0.0002	−0.001	0.0001	
(0.001)	(0.001)	(0.001)	
EDU	−0.175***	−0.188***	−0.070**	
(0.036)	(0.036)	(0.035)	
GE	0.157*	0.182**	0.193**	
(0.093)	(0.092)	(0.089)	
TI1	−0.244***	−0.261***	−0.223***	
(0.088)	(0.088)	(0.084)	
TI2	0.886***	0.937***	0.702***	
(0.086)	(0.086)	(0.084)	
GDP	4.245***	4.215***	3.739***	
(0.110)	(0.109)	(0.111)	
Green	0.033***	0.028***	0.008	
(0.011)	(0.011)	(0.010)	
Population	−0.153	−0.290	−0.456	
(2.899)	(2.889)	(2.779)	
Population_G	−0.001	−0.001	−0.001	
(0.002)	(0.002)	(0.002)	
UNLI	0.073***	0.159***	0.373***	
(0.013)	(0.023)	(0.057)	
UNLI2		−0.007***	0.038***	
	(0.001)	(0.005)	
UNLS			−0.003	
		(0.012)	
UNLI*UNLS			−0.036***	
		(0.004)	
UNLI2*UNLS			−0.001***	
		(0.0003)	
N	3329	3329	3329	
Adj. R2	0.856	0.887	0.896	
F	448.096	415.461	381.882	
Log-likelihood	−222.989	−210.874	−62.247	
Root MSE	0.271	0.270	0.259	
Joint test	11.806	11.873	12.699	
Note: *** represents the significance of 0.01, ** represents the significance of 0.05, and * represents the significance of 0.1; the parentheses below the coefficients list the robust standard errors. The regression analysis was carried out using the explained variable with a two-period lag, so the sample size N is reduced from 4136 to 3329. The meanings of variable abbreviations are shown in Table 1.

Next, this study referred to the views of Haans et al. (2016) to investigate the moderating effect of nighttime light stability on the curve relationship [52]. According to Model (3) in Table 3, the coefficient of interacion term between nighttime light stability and the square of nighttime light intensity is −0.001 and is significant at the 0.01 level. In addition, when nighttime light stability is not included, urban lighting intensity and innovation capacity present an inverted U-shaped relation. Thus, nighttime light stability will increase the steepness of the curve. On the other hand, the product of the coefficients of UNLI and UNLI2*UNLS is greater than the product of the coefficients of UNLI2 and UNLI*UNLS, so nighttime light stability will move the vertex of the curve to the right, and Hypothesis 2 is confirmed.

4.3 Robustness test

Various methods were used to conduct robustness tests. First, this study referred to the views of Lu et al. (2021) and replaced the measurement of urban innovation capacity with the invention patent grants of the corresponding city [2]. In China, the technical content of invention patents is greater than that of utility and design patents; thus, the number of invention patents can more accurately measure innovation output and innovation quality. The results in Table 4 show that after substituting the dependent variable, the significance and characteristics of the coefficients are basically in accordance with those in the main regression, and only the significance of some coefficients changes slightly. This indicates that the results of the hypothesis test are robust.Table 4 Robustness test of main effect with variable substitution.

Table 4	(1)	(2)	(3)	
Patent (the number of invention patent grants)	
UNLI	0.030***	0.068***	−0.141	
(0.005)	(0.023)	(0.163)	
UNLI2		−0.022***	−0.055**	
	(0.007)	(0.026)	
UNLS			−0.098***	
		(0.036)	
UNLI*UNLS			0.060***	
		(0.014)	
UNLI2*UNLS			−0.001**	
		(0.0005)	
Control variables	Included	Included	Included	
N	3329	3329	3329	
Adj. R2	0.792	0.838	0.846	
F	223.601	10.092	9.992	
Log-likelihood	480.051	723.425	741.907	
Root MSE	0.117	0.099	0.096	
Joint test	0.244	1.902	1.957	
Note: *** represents the significance of 0.01, ** represents the significance of 0.05; the parentheses below the coefficients list the robust standard errors. The regression analysis was carried out using the explained variable with a two-period lag, so the sample size N is reduced from 4136 to 3329. The meanings of variable abbreviations are shown in Table 1.

Second, the State Council of China issued the “Opinions on Several Policies and Measures for Vigorously Promoting Mass Entrepreneurship and Innovation” in 2015, which might affect innovation activities in Chinese cities to a certain extent. Therefore, the observation data after 2015 were eliminated, and the sample data from 2004 to 2015 were selected for the robustness test. According to the results in Table 5, after screening the observation nodes for the original data, the significance and characteristics of the coefficients of the independent variables are in accordance with those in the main effect test in Table 3, which further verifies the research hypotheses of this paper.Table 5 Robustness test of main effect with sample selection.

Table 5	(1)	(2)	(3)	
Patent (year range 2004–2015)	
UNLI	0.099***	0.198***	0.313***	
(0.012)	(0.021)	(0.055)	
UNLI2		−0.007***	0.026***	
	(0.001)	(0.005)	
UNLS			−0.047***	
		(0.011)	
UNLI*UNLS			−0.017***	
		(0.004)	
UNLI2*UNLS			−0.001***	
		(0.0003)	
Control variables	Included	Included	Included	
N	3329	3329	3329	
Adj. R2	0.884	0.885	0.883	
F	331.004	309.446	328.618	
Log-likelihood	174.008	191.572	149.031	
Root MSE	0.241	0.239	0.242	
Joint test	12.168	12.246	11.381	
Note: *** represents the significance of 0.01; the parentheses below the coefficients list the robust standard errors. The regression analysis was carried out using the explained variable with a two-period lag, so the sample size N is reduced from 4136 to 3329. The meanings of variable abbreviations are shown in Table 1.

Finally, to avoid the impact of time-consuming differences in patent output on the results, this paper further conducts a regression of urban patent grants with a lag of one period. In addition, the number of patent grants with a lag of one period met the distribution requirements of negative binomial regression; thus, the multidimensional fixed-effects model was altered to a negative binomial regression model in this robustness test. According to the results of Table 6, after changing the model setting, the significance and characteristics of the coefficients are in accordance with those of the main effect test in Table 3. These results support all the hypotheses presented in this paper.Table 6 Robust test of main effect with model change.

Table 6	(1)	(2)	(3)	
Patent (one period lag)	
UNLI	0.032***	0.465***	−0.170***	
(0.012)	(0.034)	(0.061)	
UNLI2		−0.051***	0.022***	
	(0.004)	(0.005)	
UNLS			0.122***	
		(0.013)	
UNLI*UNLS			−0.012***	
		(0.004)	
UNLI2*UNLS			−0.0003	
		(0.0003)	
ln_r	−0.022	0.073	−0.003	
(0.077)	(0.085)	(0.077)	
ln_s	3.874***	5.493***	3.876***	
(0.106)	(0.121)	(0.106)	
Control variables	Included	Included	Included	
N	3329	3329	3329	
Wald χ2	6673.264	3109.940	6017.061	
Wald χ2 p	0.000	0.000	0.000	
Log-likelihood	−24079.54	−26095.395	−24028.847	
Note: *** represents the significance of 0.01; the parentheses below the coefficients list the robust standard errors. The regression analysis was carried out using the explained variable with a two-period lag, so the sample size N is reduced from 4136 to 3329. The meanings of variable abbreviations are shown in Table 1.

4.4 Endogeneity test

Considering that there may be a mutual causal relationship between nighttime light intensity and urban innovation capacity, this study took L.UNLI (one period lag of UNLI) is an instrumental variable for UNLI to avoid the endogeneity problem. According to the results of the two-stage least squares (2SLS) regression in Table 7, the statistics applied to test the relevance and effectiveness of the instrumental variables all meet the corresponding standards, which indicates that endogeneity can be solved by the introduction of L. UNLI. Furthermore, Table 7 shows that after including the instrumental variables, the significance and characteristics of the coefficients are basically in accordance with those of the main regression, and only the significance of some coefficients changes slightly. This finding verifies the hypothesis test of this research.Table 7 Endogenous test based on 2SLS regression.

Table 7	(1)	(2)	
Patent (the number of invention patent grants)	
L.UNLI	0.094***	0.157***	
(0.007)	(0.024)	
L.UNLI2		−0.005**	
	(0.002)	
Control variables	Included	Included	
N	3329	3329	
Adj. R2	0.762	0.783	
F	1000.274	926.153	
Anderson LM statistic	26.31***	78.43***	
Cragg-Donald Wald F statistic	24.09	31.16	
Note: *** represents the significance of 0.01; the parentheses below the coefficients list the robust standard errors. The regression analysis was carried out using the explained variable with a two-period lag, so the sample size N is reduced from 4136 to 3329. L.UNLI denotes one period lag of urban nighttime light intensity.

4.5 Heterogeneity analysis

4.5.1 Heterogeneity of population density

Population density can reflect the agglomeration of talent in a specific area. Research has shown that regional talent agglomeration has a certain impact on urban construction and the vitality of urban innovation [16]. Therefore, this study examined the proxy effect of nighttime lighting on urban innovation capacity based on the heterogeneity of population density. It can be seen from Models (1) and (2) in Table 8 that when the population density is high, the relationship between the two presents an inverted U-shaped curve, which is in accordance with that in the main effect test. In contrast, when the population density is low, there is no significant association between the two variables. This shows that the proxy effect of nighttime light intensity on a city's innovation capacity can be manifested only when the urban population scale reaches a certain level.Table 8 Heterogeneity analysis based on population density and economic level.

Table 8	(1)	(2)	(3)	(4)	
High population	Low population	High GDP	Low GDP	
UNLI	0.468***	0.020	0.257***	0.036***	
(0.057)	(0.021)	(0.034)	(0.010)	
UNLI2	−0.040***	0.001	−0.011***	0.001	
(0.006)	(0.001)	(0.002)	(0.002)	
Control variables	Included	Included	Included	Included	
N	1387	1939	739	2584	
Adj. R2	0.885	0.901	0.764	0.774	
F	148.501	357.883	192.886	63.841	
Log-likelihood	−396.482	638.596	−758.407	3773.485	
Root MSE	0.340	0.183	0.687	0.060	
Joint test	10.843	10.500	2.809	12.288	
Note: *** represents the significance of 0.01; the parentheses below the coefficients list the robust standard errors. UNLI denotes urban nighttime light intensity.

4.5.2 Heterogeneity of economic level

Existing research has confirmed that cities with different economic levels have significant differences in terms of investment in image construction [53]. Therefore, this study attempted to explore the proxy effect of urban light intensity on innovation capacity in terms of economic heterogeneity. According to the results of Models (3) and (4) in Table 8, when the urban GDP is at a high level, the relationship between nighttime light intensity and urban innovation capacity manifests as an inverted U-shaped curve, which is consistent with the results of the hypothesis test. However, when urban GDP is low, there is a positive correlation between the two variables. This result indicates that only when the urban economic level exceeds a certain limit can the inhibitory effect of nighttime light intensity on urban innovation capacity be triggered.

4.5.3 Heterogeneity of the administrative grade

The administrative grades of Chinese cities can be roughly divided into four layers. The first layer includes the municipalities directly under the central government, including Beijing, Shanghai, Tianjin, and Chongqing. The second layer is subprovincial cities, which include 15 cities, such as Guangzhou, Wuhan, and Chengdu. The third layer is quasi-subprovincial cities, including provincial capital cities other than subprovincial cities. The fourth layer includes other ordinary prefecture-level cities. Currently, the Chinese government is mainly conducting the strategy of strengthening provincial capital cities, and there will be an obvious preference for the resources and policies of the first three levels of cities. Therefore, this paper divided China's prefecture-level cities into two categories, of which the first three layers were the high-administrative-grade category and the fourth layer was the low-administrative-grade category. Models (1) and (2) in Table 9 show that the proxy effect of lighting intensity on innovation capacity is not significant in cities with higher administrative grades. In contrast, the results for cities with lower administrative grades are the same as those of the main regression. This conclusion is very interesting. It may be because higher administrative grades bring more opportunities and development potential to cities, and cities often do not need to enhance their extrinsic image through deliberate lighting construction.Table 9 Heterogeneity analysis based on administrative grade and geographic location.

Table 9	(1)	(2)	(3)	(4)	
Layer 1, 2, 3	Layer 4	Eastern cities	Non-eastern cities	
UNLI	0.035	0.267***	0.159***	−0.116	
(0.102)	(0.023)	(0.036)	(0.107)	
UNLI2	0.005	−0.015***	−0.008***	0.113	
(0.005)	(0.002)	(0.002)	(0.064)	
Control variables	Included	Included	Included	Included	
N	189	3140	1486	1843	
Adj. R2	0.918	0.857	0.883	0.937	
F	33.469	219.813	175.659	521.787	
Log-likelihood	−128.304	441.424	−557.483	1760.390	
Root MSE	0.536	0.221	0.371	0.098	
Joint test	6.565	10.163	10.283	15.840	
Note: *** represents the significance of 0.01; the parentheses below the coefficients list the robust standard errors. UNLI denotes urban nighttime light intensity.

4.5.4 Heterogeneity of geographic location

Previous studies have verified that geographic location has a significant influence on factors such as urban openness and urban talent attraction [54], and the above factors are closely related to urban innovation capacity. Therefore, this study proposes that the proxy effect of urban light data on innovation capacity may show a certain degree of heterogeneity in different geographical locations. Considering that the development conditions and foundations of eastern cities are obviously better than those of other regions, this paper divided China's cities into two categories: eastern cities and non-eastern cities. According to Models (3) and (4) in Table 9, the nighttime light intensity of eastern cities has an inverted U-shaped relationship with urban innovation capacity, while the above two indicators of non-eastern cities have no significant relationship. This result further verifies that the proxy effect of nighttime light intensity on urban innovation capacity needs to be based on a certain urban foundation.

4.6 Potential mechanism analysis

4.6.1 Potential mechanism based on the industrial structure

Urban lighting can reflect the urban industrial structure to a certain extent. According to Ning et al. (2016), when the added value of services accounts for a high percentage of GDP, regional innovation capacity is inhibited [55]. According to Model (1) in Table 10, the proportion of the service industry of a city is positively related to its light intensity. This shows that nighttime light intensity can be treated as an explicit indicator of the development level of the urban service industry. Thus, it can be concluded that one of the reasons for the quadratic curve relationship between urban lighting intensity and innovation capacity may be the “encroachment effect” of service industry development on urban innovation resources.Table 10 Mechanism analysis based on industrial structure and environmental quality.

Table 10	(1)	(2)	
Proportion of service industry	Industrial SO2 emissions	
UNLI	0.254*	0.303***	
(0.144)	(0.105)	
Control variables	Included	Included	
N	4136	4136	
Adj. R2	0.865	0.750	
F	10.826	88.455	
Log-likelihood	−8821.758	−8047.570	
Root MSE	3.157	2.385	
Joint test	49.320	32.177	
Note: *** represents the significance of 0.01, * represents the significance of 0.1; the parentheses below the coefficients list the robust standard errors. UNLI denotes urban nighttime light intensity.

4.6.2 Potential mechanism based on environmental quality

Nighttime lighting requires considerable energy consumption and produces certain harmful gases. However, with the progress of society, people's requirements for the ecology and environment of settlements are constantly improving, and the quality of the environment has become a core factor affecting the charm of a city [56]. Model (2) in Table 10 shows that industrial SO2 emissions are positively related to urban nighttime light intensity, which indicates that urban lighting indeed aggravates the emissions of pollutants such as SO2. Thus, another cause for the nonlinear relationship between nighttime light intensity and urban innovation capacity may be the influence of the “pollution effect” of lighting on the attractiveness of a city.

5 Discussion

The results of the empirical analysis can effectively deepen the academic understanding concerning the proxy effect of nighttime lighting on urban innovation, and thus bring new enlightenments for relevant research.

According to the hypothesis test, the correlation between nighttime light intensity and urban innovation capacity follows an inverted U-shaped curve. This result means that the proxy effect of lighting intensity on urban innovation capacity is only established within a certain range, and when the lighting intensity is too high, this proxy effect will lose its accuracy. Most of the existing research has suggested that the extrinsic appearance (e.g., nighttime lighting) of a city is determined by its economic, technological, and cultural level [33,42]. Therefore, many studies take for granted that the better the extrinsic appearance constructions are, the stronger the city's intrinsic capacity [23,42]. However, to improve the aesthetics and influence of the city, the local government may carry out excessive extrinsic image projects, which will impose a considerable burden on the innovation and development of the city [45,47]. Therefore, when carrying out urban and regional research, it should not be simply believed that the extrinsic image of a city or region is consistent with its intrinsic capacity. In many cases, explicit observation indicators cannot effectively measure implicit capabilities and potential. The best way to avoid this problem is to conduct the necessary analysis before using a visualizing indicator, which helps to determine the deviation characteristics between the indicator and the actual situation.

Moreover, this study finds that lighting stability significantly moderates the relationship between lighting intensity and urban innovation capacity. With the increase in nighttime light stability, the inverted U-shaped curve of urban nighttime light intensity and innovation capacity becomes steeper, and the vertex of the curve moves to the right. This finding suggests that relatively stable nightlife is a prerequisite for cities to achieve high-speed innovation and development, which is consistent with the findings of studies such as Pasamar and Valle (2015) [49] and Amaral et al. (2006) [15]. In addition, a novel finding is obtained that when the light stability of a city is relatively high, a negative relationship between light intensity and urban innovation capacity will appear later, which means that the proxy effect of stable light data on urban innovation capacity is more accurate. It should be emphasized that for underdeveloped regions, urban lighting often remains relatively low for a long time. In this case, the stability of lighting may not have a significant moderating effect on the relationship between lighting intensity and urban innovation capacity [6,13]. Overall, when analyzing the intrinsic capacity of a city or region, a preliminary assessment can be made based on its constructed extrinsic appearance. However, the precondition is that a relatively stable indicator should be selected as the proxy.

Finally, the correlation between nighttime light intensity and urban innovation capacity varies with the city's geographic location, population density, economic status, and administrative grade. This shows that only when the development level of a city reaches a certain degree can the proxy effect of nighttime light intensity on urban innovation capacity be relatively accurate. A low population density and weak economic foundation weaken the relationship between nighttime light intensity and urban innovation capacity. Furthermore, when a city's administrative grade is higher, the proxy effect of nighttime light intensity on urban innovation capacity will also be suppressed. This may be because cities with higher administrative grades have more resources and opportunities [17,57]. Thus, they usually do not deliberately use lighting construction to improve urban attractiveness. In addition to the competition effect between cities, this paper further reveals two potential mechanisms that may lead to the inverted U-shaped relationship between nighttime light intensity and urban innovation capacity. One mechanism is caused by the “encroachment effect” of the urban service industry [55]. On the one hand, excessive development of the service industry may occupy limited resources in cities; on the other hand, the prevalence of the service industry indicates that the focus of urban development is not on industrial transformation and technological innovation. Another mechanism can be explained by the “pollution effect” of urban lighting on the attractiveness of the city [58]. In general, the level of urban lighting is negatively correlated with its environmental quality. Therefore, when the degree of urban lighting is too high, its living environment will also significantly decline, which may lead to some innovative talents giving up settling down in the city. The above findings not only reveal the important roles of “other factors”, such as industrial structure and environmental quality, in urban innovation and development but also provide new directions for exploring the overall coordination of urban extrinsic appearance construction and intrinsic capacity construction.

6 Conclusions

6.1 Summary of the study

Currently, innovation has become a fundamental condition for urban competitiveness and attractiveness. Cities at all levels are continuously improving their innovation capacity through various means. However, except for technical indicators such as R&D investment and patent application, existing studies on visualizing indicators of urban innovation capacity are still insufficient. As an important symbol of urban evolution, nighttime lighting data may become a visual indicator of urban innovation capacity. This paper attempts to uncover the specific relationship between nighttime lighting and urban innovation, thus filling the research gap in relevant fields. Based on panel data from 330 prefecture-level cities in China, this study used multiple regression to examine the proxy effect of lighting intensity on urban innovation capacity and further explored the indirect role of lighting stability on the above relationship. The results show that nighttime light intensity has an inverted U-shape relationship with urban innovation capacity; meanwhile, nighttime light stability moderates the relationship between the two. Moreover, the heterogeneity analysis indicates that the proxy effect of nighttime lighting on urban innovation capacity varies with urban characteristics; low population density, weak economic foundation, and high administrative grade weaken the relationship between lighting intensity and urban innovation capacity. Mechanism analysis further shows that the imbalance between extrinsic lighting and intrinsic capacity may be caused by the encroachment of the service industry and the loss of urban attractiveness. In summary, this study reveals the potential imbalance between extrinsic image and the intrinsic capacity of cities, which enriches the research on urban development and governance.

6.2 Practical implications

In addition to laying a theoretical foundation for subsequent research, the relevant results of this study can also provide practical guidance for lighting construction, innovative development, and collaborative governance of cities.

To achieve overall development, local governments must seek a balance between the city's extrinsic image construction and intrinsic capacity construction. On the one hand, when the nighttime light intensity is too low, it is difficult to show the charm and capacity of the city, which will affect the attractiveness of the city to outside talent [56,58]. On the other hand, excessive lighting construction may impose an extra burden on urban development and is not conducive to improving urban innovation capacity [47]. To this end, local governments need to rationally plan the industrial structure to avoid excessive lighting caused by the development of the tertiary industry to reduce the inhibitory effect of urban lighting construction on innovation capacity. In addition, for late-developing cities, special attention should be given to green construction and environmental management, thus effectively alleviating the pollution effect caused by urban lighting and enhancing the attractiveness and competitiveness of the city.

Furthermore, stability and a regular lifestyle are highly important for urban development and innovation. According to the conclusions of this paper, with the improvement in nighttime lighting stability, the proxy effect of urban nighttime lighting on urban innovation capacity will become more accurate. This means that regular and stable nightlife can effectively improve the work efficiency and creativity of residents [15,49], thereby improving urban innovation capacity. To this end, local governments can ensure the stability of night lighting by improving the power supply system and optimizing the construction of lighting facilities. It should be noted that constructing lighting too fast will also reduce the stability of nighttime lights. Although this may not directly affect residents’ work efficiency and creativity, it will occupy innovation resources and is detrimental to urban innovation and development.

In addition, different cities should reasonably carry out lighting construction according to their own conditions and characteristics. For cities with a low development level, the proxy effect of lighting construction is limited. For these cities, local governments should devote more resources to the development and improvement of urban innovation capacity instead of excessive lighting construction. For cities with a high level of development, urban nighttime lighting can effectively upgrade the city's appearance and present its innovation capacity [31]. Therefore, local governments should actively invest in lighting construction to enhance urban attractiveness. However, in this process, it is necessary to minimize the number of vanity projects that arise from competition and political ambition and to avoid becoming a flashy city.

6.3 Limitations and future research directions

This study has the following limitations. First, the measurement of urban light stability needs to be optimized. Due to the availability of data, this paper does not consider the differences in spatial distribution but directly measures the stability by the variance of nighttime light data. However, the dispersion of nighttime light may also affect the variance in light data; therefore, future research could expand the data range and improve the accuracy of urban light stability measurements by analyzing changes in nighttime light at the county level. Second, the theoretical framework of the study needs to be expanded. Although this paper subdivides the urban light indicators into light intensity and light stability, the subdividing dimensions of the urban light indicators are far greater. Future research can develop the dimensions of urban nighttime lighting from the perspectives of spatial distribution and color diversity and expand the theoretical framework of existing research accordingly. Finally, by reviewing the relevant research, this study found that when examining the relationship between urban lighting data and urban development indicators, most of the studies, including this study, used empirical analysis. In fact, there are strong differences in urban lighting construction. Therefore, relevant research can combine specific urban cases to analyze the proxy effect of night lighting data on urban innovation and development.

Data availability statement

Data will be made available on request.

Finding sources

This study was supported by the 10.13039/501100001809 National Natural Science Foundation of China (grant no. 72372059 ) and the Humanities and Social Science Fund Project of China's 10.13039/100009950 Ministry of Education (22YJA630116 ).

CRediT authorship contribution statement

Zhenyu Jiang: Writing – review & editing, Writing – original draft, Visualization, Software. Zhubo Li: Writing – original draft, Methodology, Formal analysis, Data curation. Jianhua Wang: Validation, Data curation, Supervision.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

The authors would like to acknowledge Prof. Zongjun Wang for his suggestions during the revision process.
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