Genetic testing has evolved rapidly over recent years and new developments have the potential to provide insights that could improve the ability to diagnose, treat, and prevent diseases. Information obtained through genetic testing has proven useful in other specialties, such as cardiology and oncology. Nonetheless, a range of barriers impedes techniques, such as whole-exome or whole-genome sequencing, pharmacogenomics, and polygenic risk scoring, from being implemented in psychiatric practice. These barriers may be procedural , economic , or related to clinicians’ knowledge, attitudes, and practices . Additionally, several ethical concerns may arise . Here, we provide an overview of potential barriers for the implementation of genetic testing in psychiatry, as well as an in-depth discussion of strategies to address these challenges. Genetic testing has evolved rapidly over recent years . New tecIndications for genetic testing vary by disorder. Given the current evidence and its widespread professional support we highlight examples of clinical testing indications for autism spectrum disorders (ASD). However, evidence to support direct-to-consumer testing will require further investigation for all psychiatric disorders. Regarding polygenic risk scoring (PRS) and pharmacogenetics, evidence is increasing rapidly, with high potential for future clinical translation of both, such as for diagnostic purposes and pharmacological interventions , 9.To date, genetic testing has been implemented most extensively in oncology and cardiology. For example, multigene panel testing for hereditary cancer predisposition, including breast, ovarian, and colorectal cancer, has been readily incorporated into clinical practice –12. Due In cardiology, DNA-sequencing is widely used for the diagnosis and clinical management of heritable heart diseases, such as hypertrophic cardiomyopathy and long QT syndrome, with a diagnostic yield of genetic testing in the range of 30–50% and 60–70%, respectively . Recent CYP2D6, DPYD, and TPMT genotypes in oncology [CYP2C19, CYP2C9, SLCO1B1, and VKORC1 genotypes in cardiology [Oncology and cardiology are leading fields in the implementation of pharmacogenetic testing. The Clinical Pharmacogenetics Implementation Consortium (CPIC) has produced prescribing guidelines for various drugs according to oncology –21, and rdiology –24.CYP2C19 and CYP2D6 genotypes [CYP genotyping to genome-wide association studies (GWASs) as an important source of pharmacogenetics data. GWASs have proven successful in identifying complex pharmacogenomic traits in medicine, including psychiatry [In psychiatry, genetic testing can be used to diagnose underlying genetic syndromes  and—in research settings—to provide insight into prognosis and treatment response, particularly for disorders with high heritability estimates, such as ASD, attention deficit and hyperactivity disorder, schizophrenia, and bipolar disorder , 26. Theenotypes , 38. Howychiatry . The larychiatry . Finallyychiatry –43.Targeted genetic testing may be done to confirm a suspected diagnosis based on phenotypical or clinical features, family or personal medical history, such as in Duchenne muscular dystrophy and Fragile X syndrome , 45. Usithis genetic etiology”), broad genetic testing addresses the question of genetic causation more broadly (“a genetic etiology”) [While targeted genetic testing answers a defined hypothesis (“iology”) . Broad tiology”) .As next-generation sequencing gradually becomes less expensive, WGS and whole-exome sequencing  are becoming more and more feasible options in clinical practice . HoweverWith ever-evolving technologies, it is essential to monitor and continuously evaluate whether tests meet the requirements to be considered sufficient to be implemented in clinical practice . In geneASD and intellectual disability (ID), collectively referred to as neurodevelopmental disorders (NDD), at present qualify as the only psychiatric disorders with enough evidence supporting genetic testing as part of standard clinical practice. Chromosomal microarray analysis (CMA) has been offered as a diagnostic tool for developmental delay as well as ASD for some years  , 58. NowFurthermore, when evaluating the clinical utility of genetic tests, special consideration must be given to risk. The effect size of risk (or resilience) on a group level, traditionally represented as the odds ratio (OR), must be translated to measures of individual risk, such as PPVs and NPVs. Although group- or population-level effect sizes may appear substantial, their clinical translation requires the application on an individual level, i.e., a translation that represents the individual risk of the patient, rather than the complete at-risk population .The field of psychiatric genetics has advanced tremendously over the past 20 years, with high potential for diagnostics, prognosis, and treatment , 25, 65.loci have been discovered to be associated with various diseases, including psychopathological traits [With the advent of GWASs, hundreds of new genetic l traits  and psycl traits , 68, andl traits –71. WhilPRS can be considered as a measure of the cumulative impact of hundreds to thousands of individually weakly associated common genetic variants , 73. As loci previously implicated in either ASD and/or ID [While the risk for most psychiatric disorders has been shown to be influenced by many common, low-risk variants (as outlined above), rare and highly penetrant variants can also play a role. Even though each rare variant explains only a fraction of disease vulnerability in the population, on an individual level, they confer a much greater risk of developing a certain disorder than the risk predicted by PRS. For example, the risk for ASD in individuals with a 3q29 deletion or a 7q11.23 duplication is estimated to be 38% , 85 and nd/or ID . Finallynd/or ID , 87, 88.HLA-DQB1, HLA-B, and SLCO1B3/SLCO1B7 [Genetic variants, such as single-nucleotide variants (SNVs), have been associated with a higher risk of adverse drug reactions to psychotropic medications, such as antipsychotics and antidepressants . For exa/SLCO1B7 –95. The /SLCO1B7 , 96–98. /SLCO1B7 .p values<5.0×10−8) [p value=0.03, hazard ratio=3.8) [Moreover, investigating the clinical usefulness of genetic testing for indications is also relevant for lithium, given the high variability in response, the narrow therapeutic window, the potential severity of side effects, and the associated current underuse of this drug. In the largest lithium response GWAS to date by The International Consortium of Lithium Genetics (ConLiGen), a single locus of four linked SNPs on chromosome 21 was significantly associated with lithium response  . The samtio=3.8) . Anothertio=3.8) . Howevertio=3.8) .CYP3A4 and CYP2D6 [CYP3A4 is implicated in the metabolism of over 50% of commonly prescribed psychotropic drugs, including antipsychotics, antidepressants, anxiolytics, and mood stabilizers [CYP2D6 enzymes mediate the oxidative metabolism of at least 30 psychotropic medications [Furthermore, over 50 cytochrome P450 enzymes are key for the metabolism of several medications, with 90% of all medications being metabolized by six of them, especially d CYP2D6 . CYP3A4 bilizers , and CYPications , 104. Adications , 106, whications .CYP2D6 and CYP2C19 genotypes [CYPD26 genotypes [CYPD26, OPRM1, and COMT genotypes [HLA-A and HLA-B genotypes [Finally, clinical guidelines have been developed by the CPIC on the prescription of selective serotonin reuptake inhibitors and tricyclic antidepressants by enotypes , 38; atoenotypes ; opioid enotypes ; and carenotypes .Although promising, many of the abovementioned techniques and methodologies are not yet ready for direct implementation in the clinic. Below we elaborate on and analyze several barriers to the implementation of genetic testing in clinical psychiatry Fig. , so thatSeveral methodological challenges currently stand in the way of the applicability of genetic testing at a patient level in psychiatry. First, the effect sizes and the explained variances of PRS at this moment are small, hampering their utility for individual risk prediction . This inSecond, there is still uncertainty about whether findings from studies can be extrapolated to people of different ancestries as risk differences attributable to ancestry may differ up to 10-fold . So far,Pharmacoeconomic research has shown conflicting evidence regarding cost-effectiveness of genetic testing . Early sIn the United States of America (USA), physicians have historically considered funding a considerable barrier to the use of pharmacogenomic testing in clinical practice , and forApart from implementation costs, some studies have also identified perceived pragmatic barriers to the implementation of genetic testing, such as infrastructure, human resources, and sustainability , 123. ThStudies show that clinicians see the potential benefits of using genetic testing, such as guidance in therapeutic decision-making and a positive impact on patients’ motivation and adherence, but they also mention several barriers , 127. ThAnother significant barrier to the adoption of genetic testing is the lack of general understanding of genetics, probability and risk prediction by patients, families, and clinicians themselves .Genetic knowledge is also seen as advancing at an accelerating pace. What is standard practice at the start of a clinician’s residency may already be outdated by the end of it. This rapid change and advancement may cause clinicians, including psychiatrists, to feel uncomfortable making decisions about which tests to order, interpreting the results, and most importantly, communicating such results to patients and families .Finally, genetic etiology has not been incorporated into most psychiatric diagnostic systems, e.g., the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). Classification of most psychiatric disorders, such as schizophrenia, still relies solely on clinical signs and symptoms. Of note, the identification of a ‘medical’ cause is explicitly formulated as an exclusion criterion for most diagnoses, such as schizophrenia. This implies that people who meet the schizophrenia inclusion criteria and have an identified genetic etiology  formally cannot be diagnosed with schizophrenia .Obtaining genetic risk information may also carry negative consequences for patients and their family members. First, there is the risk that patients and relatives may misinterpret complex genetic information. For example, when it is stated that “addiction is 50% genetic in origin”, this can be understood in several ways. Families may understand that relatives have a 1 in 2 chance of developing a similar disorder or that a lack of positive family history somehow confers immunity . ClearlyPsychological side effects of genetic testing include anticipatory fear and anxiety, particularly when a positive test result is expected and its implications are feared , 134. AfLastly, commonly reported concerns with genetic testing include stigmatization and discrimination. Patients with psychiatric disorders are already among the most stigmatized groups in society, which can impair help-seeking and quality of treatment, and can lead to feelings of exclusion , 138. FeAdequate care after genetic testing, including support groups or psychological follow-up, is pivotal for both patients and relatives to cope with results , 140. ThBelow we outline recommendations to overcome the barriers discussed in the previous section. This is not meant as an extensive list and as new insights develop, undoubtedly new avenues to address such challenges will ensue.From medical school to medical specialty training, the acquisition of appropriate genetics knowledge, skills, and attitudes should be encouraged. This is of paramount importance given the role of psychiatrists in providing support and management to patients and families with, or at risk of, highly heritable psychiatric conditions . Such edPsychiatrists should always be aware of and assess the emotional, ethical, legal, and social impact of genetic information on patients and their families . This caFurthermore, the International Society of Psychiatric Genetics formed a Residency Education Committee to identify key genetic knowledge to be taught in psychiatry training programs , 143. FoOn a similar note, training residents in the genetic aspects of mental health would encompass a wide range of clinical benefits. For example, specific training may raise residents’ awareness of genetic risk, allow for community support to patients and families, and facilitate reproductive counseling and family planning to parents with affected children. In addition, training programs may enable residents to make better informed medication choices to reduce the risk of severe medication side effects –144.Initiatives such as PDGENE , an ongoIt is important to make psychiatric genetic counseling services culturally appropriate, socially and financially accessible, and ethically coherent in order not to further alienate already underserved populations . EspeciaWe believe there is also a pressing need to help shape public mental health policies and clinical guidelines, by informing both public health systems and private insurance companies about tests that have shown beneficial clinical applicability, such as pharmacogenomic testing in cases of repeated nonresponse or high susceptibility to side effects. Factors considered by insurers when formulating medical coverage policies for pharmacogenomic testing include availability of clinical guidelines, use by physicians in current clinical practice, cost-effectiveness, and patient interest . MoreoveCYP2C9, CYP2C19, CYP2D6, HLA-A, HLA-B) [Commercially available pharmacogenetic tests are becoming increasingly accessible due to reduced pricing and simplified implementation procedures . For exa, HLA-B) . Such a , HLA-B) , 161.CYP2D6 and CYP2C19 [In addition, some commercially available pharmacogenetic test panels may be well equipped to facilitate the implementation of most pharmacogenomic dosing guidelines relevant to psychiatry, including those associated with  CYP2C19 , 162. Ho CYP2C19 . A stand CYP2C19 , 164.Regarding the current lack of integration of genetic etiology in the DSM-5, one way to close this gap is by adding genetic etiology as a specifier to the diagnosis, in addition to the symptom-based diagnostic criteria, as has been suggested for ASD . By inclWe also signal a need to leverage the potential of genetic findings for diverse patient populations. The past years have indeed witnessed an increase in GWASs of mixed populations by the Psychiatric Genomics Consortium, as well as the coming into existence of genetic studies in currently underrepresented populations, as exemplified by the Latin America Genomics Consortium. Further advancing such diversity will facilitate greater PRS accuracy in populations of non-European ancestry , 113. ByAdditionally, it is necessary to translate group level findings to individual risk prediction metrics to increase the clinical relevance of PRS , 75, 165First, we propose an update on current diagnostic guidelines that build on previous efforts, analogous to those published for ASD and ID , 167. A Moreover, genetic testing and counseling may be included in guidelines of psychiatric associations across the globe . These gFor successful implementation of clinical genetic testing, it is essential that patients, families, and caretakers’ associations are involved in the process of development, implementation, and evaluation of genetic testing. These key stakeholders should be actively empowered and encouraged to provide voices and input that shape public mental health policy, clinical guidelines, and research proposals. By doing so, barriers to access genomic testing and genetic counseling may be overcome. Genetic counseling for psychiatric disorders has proven to be effective in increasing empowerment in both patients and family members , 173. WeWith the advancement of new genetic testing methodologies, more discoveries can be made at a rapid pace in the field of psychiatric genetics. Several challenges currently hamper the implementation of psychiatric testing, be it broad or more targeted genetic testing in clinical settings. We are optimistic about the implementation of genetic testing in clinical psychiatry around the world as a variety of recommendations can be followed to overcome such barriers. To achieve this, it will be essential that all relevant stakeholders, and especially patients and family, are actively involved. We encourage future research projects to investigate the potential beneficial effects of these recommendations on genetic counseling settings and the quality of life of patients and their relatives around the world.


We study the collapse and expansion of a cavitation bubble in a deformable porous medium. We develop a continuum-scale model that couples compressible fluid flow in the pore network with the elastic response of a solid skeleton. Under the assumption of spherical symmetry, our model can be reduced to an ordinary differential equation that extends the Rayleigh–Plesset equation to bubbles in soft porous media. The extended Rayleigh–Plesset equation reveals that finite-size effects lead to the breakdown of the universal scaling relation between bubble radius and time that holds in the infinite-size limit. Our data indicate that the deformability of the porous medium slows down the collapse and expansion processes, a result with important consequences for wide-ranging phenomena, from drug delivery to spore dispersion. Significance StatementCavitation, the liquid–vapor phase transformation of a fluid driven by depressurization, is critical in many science and engineering applications, but it also occurs in our daily lives, for example, when we crack our knuckles or when sea waves break at the beach. Past research has focused on cavitation in free or wall-bounded fluids, but cavitation processes that occur in a soft porous material remain unexplored. Here, we develop a computational model that shows that the deformability of a porous material slows the collapse and expansion of cavitation bubbles and breaks down the classic scaling relation between bubble size and time. Our results have profound consequences for diverse phenomena that involve cavitation in soft porous materials, from traumatic brain injury and drug delivery to spore dispersion.The collapse of cavitation bubbles in free fluids has fascinated scientists for decades, but what happens when a cavitation bubble collapses in a soft porous material? Cavitation processes are ubiquitous in physics ,2, enginHere, we study the collapse and expansion of a cavitation bubble in a poroelastic material. We formulate a new continuum model that couples compressible fluid flow in the pore network with the elastic response of the solid skeleton. Our theoretical and computational results show that the elasticity of the solid skeleton slows down the collapse process, especially in the final stages. One of the primary driving forces for bubble collapse in a free fluid is the pressure difference between the bubble interior and the far field . In a pot, p = F(ρ), where p is the pore pressure andpsat is the saturation pressure, ρc is the density that leads to minimum speed of sound, 2C/ρc is the bulk modulus of the fluid at minimum speed of sound, and b−1 is the fluid’s covolume. The speed of sound predicted by eq.  is the mixture’s dynamic viscosity. We take μ(ρ) = μv + (μl − μv)(ρ − ρv)/b, where μl and μv represent, respectively, the viscosity of the liquid and gas, and ρv is a representative density of the vapor phase such that psat = F(ρv). We assume that the collapse process is primarily driven by the difference between a preimposed, far-field static fluid pressure in the liquid phase and the static fluid pressure in the bubble’s interior, thus neglecting any surface tension effects, which can be shown to be small for the cases studied in this work  = ρF′(ρ) and Substituting eqs. , 3), an, an3), a\documentnematics .M = (λ + 2G) is the P-wave modulus, r is the radial coordinate, and ur the solid displacement in the radial direction. Integrating eq. (Mϵv − αp = g(t). The function g can be determined using the boundary conditions. We will focus on the boundary condition ρ = ρL, where L is the radius of the spherical poroelastic medium and ρL is a given density. Using the boundary condition ϵv = 0 for the linear momentum equation, we obtain g = −αpL, where pL = F(ρL). Taking the time derivative of the expression Mϵv = α(p − pL) and substituting in eq. (1 − 2ν)], G = E/[2(1 + ν)], and M = E(1 − ν)/[(1 + ν)(1 − 2ν)].We initially investigate the collapse of a spherical cavitation bubble in a poroelastic medium see Fig. . Under t\documentr ∈ , where L = 1 mm. The model parameters that are kept fixed for all of the simulations in this paper are shown in Table L/2. The density at r = L is set to ρL = 998 kg m−3, while the density in the vapor bubble is ρ ≈ ρv. Figure R(t) for different values of the solid skeleton Young’s modulus. For each value of E, we observe a monotonic decrease of the radius with R reaching zero at the collapse time. After collapse, the fluid remains in liquid state without any regrowth of the bubble. This was expected and is consistent with results of other models that consider collapse of cavitation bubbles in free liquids without accounting for noncondensable gases (NCGs)  . The datE = 15 kPa. The time evolution of the fluid density is shown in Fig. Figure α = 1) and that the problem is spherically symmetric. The bubble is centered at r = 0, and its radius R depends on time. Let us restrict eq. , where the fluid is in liquid state and we can assume that the density is approximately constant. Under these assumptions we obtainS is an arbitrary function of time andr = R and identify the fluid velocity at the bubble surface with R, L), where the fluid density can be assumed to be constant, we havep(R) = pB. Herein, pB is the pressure in the bubble. To transform the right-hand side of eq. , its last term involves values of the density on the entire domain r ∈  with s > R. Thus, the assumption of constant density that we made for the liquid phase is no longer valid here. Instead, we assume that the density is a traveling wave of the form ρ = h(z − R(t)). There are multiple options for the function h, but the simplest one is a step function that transitions from ρv to ρl at the bubble surface, i.e. ρ = ρv + (ρl − ρv)H(z − R), where H is the Heaviside function. This assumption allows approximate integration of the last term in eq.  for the time evolution of the bubble radius. The ODE is an extension of the approach used by Rayleigh  and Plesrict eq.  to the de obtain\documentrom eq. . Eq. (η = 1.4 for diatomic gases. We model the ultrasonic excitation by taking the pressure boundary condition as pL(t) = F(ρL) − ( −1)spamp sin(2πft), where pamp = 1,000 kPa is the ultrasonic wave amplitude, f = 20 kHz is the wave frequency, and the constant s = 1, the bubble collapses first, and then it expands; and when s = 2, the bubble expands first, and then it collapses. Figure s = 1 and R(0) = 100 μm. We initially observe a bubble collapse, which is slower in the poroelastic medium (see inset). The bubble remains small and compressed for a time interval, and then it expands. The expansion velocity and the maximum bubble size are smaller in the poroelastic medium. Figure R over six periods of the pressure wave for s = 2. In this case, the bubble expands prior to any collapse. The expansion velocity and maximum bubble size are smaller in the poroelastic medium as before. Here, we can also see that the collapse is stronger and the bubble reaches a smaller size in the rigid medium.We use the poroelastic Rayleigh–Plesset equation to study the dynamics of a bubble under ultrasonic excitation. To avoid singularities in the collapse and to be able to study multiple cycles of expansion and collapse, we assume that, in addition to vapor, the bubble contains an NCG, such as nitrogen. We model this by taking the pressure in the bubble as\document−3 in the bubble interior to 998.2 kg m−3 at the left, right, and top boundaries, where its value is imposed. The solid displacements are set to zero at the bottom boundary. All other boundaries are subject to traction-free conditions. Figure E = ∞ kPa) at the same time relative to the collapse time, that is, In many cases of practical interest, such as drug delivery, cavitation bubbles collapse near a solid surface, instead of in isolation. We study bubble collapse near a solid using axisymmetric simulations in cylindrical coordinates of eqs.  and 7)..7). FiguIn conclusion, cavitation processes occurring in soft porous media had remained unexplored, despite their common occurrence in physics, science, and engineering. The results presented herein show that the collapse and expansion of a cavitation bubble is much slower in a soft porous medium than in a rigid medium. Our model indicates that this occurs because elastic forces reduce the fluid pressure gradients at the bubble interface. The slower collapse has important consequences for technological processes that rely on the violent collapse of cavitation bubbles, such as cavitation-triggered spore dispersion  and lipopgac150_Supplemental_FileClick here for additional data file.


Resource-based cities are suffering from resource scarcity and environmental deterioration. Spirit, vitality and prosperity are disappearing and cities have moved towards “the valley of death” in terms of urban development. This typically appears in environments where it is difficult to maintain sustainable development. Based on empirical analysis, a qualitative analysis method for the selection of evaluation indicators, as well as a quantitative analysis method for index weighting and principal component extraction for constructing a three-level evaluation index system of green development for coal-resource-exhausted cities, was adopted. This study also discussed the life cycle at different development stages of resource-based cities, including mature resource-based and growing resource-based cities. We further argued that the obstacle degree can act as an evaluation basis and make recommendations accordingly to improve the green development of cities. Through star-standard divisions and statistical analysis, it can be explicated that the increase in green development in the first stage is greater than that in the later stage, which is more obvious in cities with lower stars. The results also show the evolution trend and stability coefficient. There is no end in sight for urban green development, and this study can provide a new perspective to relieve the declining trend and promote green sustainable development. In the process of human development, humans have adopted an extensive development model with “high input, high consumption and high pollution” ,2,3, conIn recent years, the COVID-19 pandemic has also threatened city renewal and energy consumption , and eneChina is a country with a large amount of coal resources compared with the rest of the world. The resource-based cities that rely on coal resource mining and primary processing account for one-third of the cities in China, and they have had a great effect on social and economic development in the past ,24. HoweTo date, scholars have carried out much research on the green transformation of cities, but there is still a lack of studies on this topic to date. First, based on the characteristics of typical coal-resource-exhausted cities, which are distributed in different regions and have different regional characteristics, this can reflect the impact of regional differences on urban development to a certain extent, which has rarely been mentioned in other studies. Second, stage characteristics of the evolution and life cycle of coal-resource-exhausted cities are discussed in this study, whereas few have considered this in previous research. Third, the coordination between qualitative analysis and quantitative analysis methods adopted in this study can show the research results more deeply. This study attempts to obtain the contribution rate, cumulative contribution rate and principal component load by calculation, which have been rarely mentioned previously. Furthermore, although some prior research has focused on the evaluation index system of green development, we further analyzed the obstacle factors and obstacle degree of green development on resource-exhausted cities by empirical analysis.Hence, in order to thoroughly understand the green development and transformation of resource-exhausted cities, in our preliminary study, we evaluated the relevant literature to determine whether they applied to resource-based cities, resource-exhausted cities, economic transformation, green transformation, green development, etc., and summarized the specific research direction. According to the distribution of resource-exhausted cities in three different years , and the planning document (GF [2013] No. 45) issued by the State Council on 12 November 2013, a total of 33 cities in China were defined as coal-resource-exhausted cities and selected for field investigation as the typical cases, which lasted for two years, to obtain data on green development. Furthermore, we also discussed the issues with experts to collate their suggestions regarding the green development of resource-exhausted cities. We further discussed the life cycle and development stages of resource-exhausted cities, then analyzed and constructed the evaluation index system and obstacle degree of green development. It was expected that this would allow us to explore green development suitable for coal-resource-exhausted cities on the basis of understanding the situation concerning economic and social development versus ecological and environmental protection.This paper is organized as follows: According to the level of green development, resource-based cities can be divided into three types, that is, mature resource-based cities, growing resource-based cities and resource-exhausted cities. Resource-exhausted cities refer to cities in which the development of mineral resources has entered the process of decline or depletion ,29. TherGreen transformation refers to the transformation of the development model to sustainable development with the construction of the ecological environment as the guide , circulaThe transformation of resource-exhausted cities is also a prominent phenomenon experienced or being experienced in the economic and social development of all countries ,46. Not In the early and middle stages of industrialization, it had already been a representative problem. Europe, the United States and Japan have had very developed mining industries in the modern period, which also created a developed mining economy and formed an entire legal system and civilization, such as the Ruhr mining area in Germany and Lorraine mining area in France , both ofFrom After a few decades of development, China’s resource-based cities are gradually being threatened by resource depletion . SimilarGreen development is regarded as an important part of scientific development; the concept of green development converges with the essence of an “ecological civilization” ,55, “greBlueprint for a green economy”  No.45 and established 33 coal-resource-exhausted cities, accounting for more than half of the total number of cities with declining resources; this shows that the development of coal-resource-based cities is not sustainable. According to the list of resource-exhausted cities published by the National Development and Reform Commission in 2008, 2009 and 2011, China Statistical Yearbook (2020) , we seleThe 33 coal-resource-exhausted cities relate to seven geographical regions in China. Influenced by history, mining technology and other developmental factors, they are mainly concentrated in Northeast China (10 cities), East China (6 cities), North China (5 cities), Central China (5 cities), South China (1 city), Southwest China (3 cities) and Northwest China (3 cities), involving 19 provinces. Urban planning departments generally classify cities according to the total population number , and cities can be divided into four levels in size: small cities, medium-sized cities, large cities and mega cities. According to the standard of city size classification in China, there are 3 mega cities, 5 big cities, 6 medium-sized cities and 19 small cities in terms of the coal-resource-exhausted cities in China .Green development emphasizes the rules of nature, maintaining the ecological environment and developing green ecological industries. It aims to build an industrial chain of resources, the environment, products, regeneration, utilization and recycling, and realize the coordination and unity between man and nature, economy and society ,65. Ther(1)Resources and the environment. The resources and the environment are the material basis for human survival and societal development. This indicator mainly reflects the field of the ecological environment, paying attention to the impact of economic development. In particular, this includes the impact of industrial discharge on the quality of the ecological environment, the impact of waste and wastewater treatment in daily life on the urban environment and the impact of urban greening on improving the quality of the living environment for urban residents .(2)Economic development. Economic development is accompanied by the growth of the economic aggregate, and the increase in the economic aggregate can drive an increase in people’s income. As the economy increases, it will drive changes in economic and financial structures. Therefore, the measurement of economic development mainly includes four aspects: the growth of regional GDP, the improvement of income, the investment of science and education and the optimization of economic structure ,77.(3)Population and social welfare. The population in the evaluation index system refers to the population living in cities and market towns. Social welfare mainly focuses on human development and conducts an all-round investigation from the perspectives of transportation, education, medical treatment, etc. . The invWith regard to the selection, based on the National Sustainable Development Plan for Resource-based Cities (2013–2020) (GF [2013] No. 45) issued by the State Council and the China Statistical Yearbook of Cities in 2020 , the urbOn the basis of selection of the criteria-level indicators, the indicators needed to be extended to the index layer. Therefore, according to the actual situation of coal-resource-exhausted cities and following the design principles of the evaluation index system of urban green development, 23 secondary indicators were selected, corresponding to three evaluation criteria. The secondary indicators selected in this study have the following characteristics: First is the consistency between secondary indicators and primary indicators. The secondary indicators need to be selected based on the requirements of the primary indicators, that is, the criteria layer, which can reflect the purpose of the primary indicators and is closely related to the content of the primary indicators. Second is the pertinence of secondary indicators to specific cities. Coal-resource-based cities have their unique urban structure and judgment standards; thus, while selecting the secondary indicators, the pertinence of specific urban types should be focused on so that the indicators can more effectively reflect the actual situation of target cities. Third is the operability of data collection of indicators samples. The secondary indicator is the most specific indicator layer in this study, which needs to be weighted by objective methods. Therefore, the selection of secondary indicators must be supported by corresponding sample data. Fourth is the proportional treatment of secondary indicators. When obtaining sample data, there are various unit forms and the scale of each sample is different, which cannot simply be compared directly. Therefore, in the preliminary data process, the sample data were scaled to convert them into a proportional form so as to partially eliminate the influence on data units and city scale. In view of the above requirements and characteristics, the index layer of this study was able to convey the purpose of the criterion layer according to the specific situation of coal-resource-exhausted cities and provide operable sample data for the objective weighting of indicators. The specific index system is shown in The reason for the green transformation of resource-based cities is the result of the systematic operation of three factors, namely resources and the environment, economic development, and population and social welfare. Internal contradictions of the four factors constitute the direct driving force of the green transformation of resource-based cities. Therefore, evaluating the green development from these four factors is helpful to relieve the contradiction between the unlimited demand in economic and social development and the limited supply in terms of the operation of resources and the environment. We also analyzed the intensification and mitigation of the contradiction. The specific methods of realizing the green development of resource-based cities are via the green transformation of enterprises, industries and governments, which are directly related to the green transformation effect of resource-based cities at the microcosmic, middle and macroscopic levels. To build a green city is to create a low-carbon development paradigm that runs the concept of green development through the whole process of construction, and takes green production as the basis, green life as the driving force, green ecology as the guarantee and green life as the vision to realize the development paradigm that is suitable for industry, living, entertainment and tourism.Determining the weight is the second most important element in constructing an evaluation index system of the green development of coal-resource-exhausted cities. In order to ensure the objectivity of the weight determination process, this study used the principal component analysis method to calculate the weight of 23 indicators describing the green development of coal-resource-exhausted cities.Principal component analysis (PCA) is a mathematical transformation method that simplifies the complex relationship between variables . While aThe advantages of using principal component analysis to process multivariable panel data are as follows: First, it can remove the influence of the correlation of the original indicators on the information repetition caused by the results, so as to ensure the objectivity of the results. Second, the weight of each index produced by the principal component analysis in the operation process is completely based on objective information, which is different from the analytic hierarchy process and can avoid the bias and error caused by subjective factors. Third, it can reduce the workload on the premise of retaining most information as much as possible.(1)Samples and data sourcesThis study took 33 coal-resource-exhausted cities identified in the No. 45 Document of the State Council [2013] as samples, and selected the China Statistical Yearbook  from 200(2)Dimensionless data processingWhen using principal component analysis, the dimensionless processing of the original data and the impact of positive and negative indicators on the calculation results were focused on. Due to the differences in the index units of the selected evaluation indicators, that is, the data having a dimensional effect, the selected data should be processed as dimensionless. The commonly used dimensionless processing methods mainly include standardization, extremum, etc. However, since the data after standardization can only reflect the impact between indicators and cannot reflect the differences in the degree of variation between indicators, compared with extremum processing methods, it is not suitable for dimensionless processing among multiple variables. Therefore, this study selected the extremum method to process the data as dimensionless.Since the principal component analysis needs to be unified into positive indicators, the negative indicators in the original indicators should be handled separately. In this study, the negative indicators of the green development of coal-resource-exhausted cities, namely the emission of industrial dust of GRP per CNY 100 million, the emission of industrial sulfur dioxide of GRP per CNY 100 million and the industrial wastewater emission of GRP per CNY 10,000, were dimensionless and treated with Formula (2); the other positive indicators were treated with Formula (1).The specific formula is as follows:(3)Data validity testBefore using data samples, it is necessary to test the adequacy of data and samples. The results of the KMO test and Bartlett’s test of sphericity are shown in p-value was 0, which is far less than 0.05. This shows that the sample data were suitable for principal component analysis.This study uses Stata 13.0 software to conduct the KMO test, and it was found that KMO = 0.589, which is >0.5; the observed value of Bartlett’s test of sphericity was 10,479 and the corresponding (4)Calculation of principal component analysisIt is assumed that the data used had several samples, that is, Calculating the correlation coefficient matrix,We calculated the eigenvalue of the characteristic equation, the eigenvector of the corresponding eigenvalue, principal component contribution rate, cumulative contribution rate and principal component load. The eigenvalue, contribution rate, cumulative contribution rate and principal component load were obtained by calculations, which are shown in It can be seen from the calculation of the eigenvalue and variance contribution rate that the top 12 components contributed 85.8% (more than 85%), that is, the top 12 components could replace 23 original indicators. Therefore, the top 12 components were extracted, namely, principal components 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 and 12.According to The normalized weight was calculated according to the coefficient of each index in the principal component, as follows:The normalized weight was:Hence, the normalized weight of each indicator was obtained by substituting the data and the index evaluation system of the green development for coal-resource-exhausted cities was constructed, as is shown in After obtaining the specific index weight, the evaluation model of green development was further used to evaluate the green development level of 33 coal-resource-exhausted cities from 2005 to 2015. The higher the evaluation index The score of the green development level of 33 coal-resource-exhausted cities in China from 2005 to 2015 can be obtained by substituting the index weight into Equation (7), as is shown in Based on the above analysis, we calculated the score and rank of the green development of coal-resource-exhausted cities in 2005, 2010 and 2015 .The specific situation of the green development level of each coal-resource-exhausted city is shown in Zibo was the city with the highest average level of green development and Laibin was the lowest. By calculating the average value of the three years, it was found that the three cities with the highest average level of green development were Zibo, Lanzhou and Xuzhou, reaching 0.56, 0.54 and 0.53, respectively. The three cities with the lowest average level of green development were Laibin, Chaoyang and Chengde, reaching 0.38, 0.39 and 0.41, respectively.The city demonstrating the fastest change was Wuhai, and the city with the slowest change was Hegang. Over 11 years, the score ranking of green development in Wuhai increased by 23 places, which is 52% higher than that in 2005; Shizuishan’s score ranking of green development also increased by 14 places, including 6 in the first five years and 8 in the next five years; Shaoguan increased by 13 places, characterized by rapid green development in the first five years, rising 11 places, and then rising 2 places in the last five years, which is slightly weak. Hegang, the city with the slowest improvement, fell by 20 places; Qitaihe and Jingzhou also showed slow changes, falling 18 and 13 places, respectively. Although these cities have slowly risen, their level of green development has always improved; however, some cities have regressed in terms of their green development, such as Baotou and Jingzhou, which had a lower level in 2015 than in 2010.Moreover, we also used the median method to grade the evaluation scores of the green development of coal-resource-exhausted cities in China from 2005 to 2015, divided into four levels: one star, two stars, three stars and four stars. The specific corresponding division and state of the green development level are shown in In order to understand the green development of coal-resource-exhausted cities more intuitively, the data of 2005, 2010 and 2015 were extracted and analyzed.As is shown in First, the level of green development of 33 coal-resource-exhausted cities has steadily improved. From the minimum value, the scores in 2005, 2010 and 2015 were 0.297, 0.385 and 0.439, respectively; from the maximum value, the three-year scores were 0.481, 0.574 and 0.629, respectively; from the average score of green development, the three-year average scores were 0.394, 0.474 and 0.514, respectively. Therefore, the level of green development of coal-resource-exhausted cities in China steadily improved from 2005 to 2015.Second, the increase in green development from 2005 to 2010 grew faster than that from 2010 to 2015. In 2005, 2010 and 2015, 11 years were separated into two stages. Comparing the two-stages of green development, it was found that no city reached a four-star level in 2005 and nine cities reached a four-star level by 2010, while 19 cities were at four-star green development in 2015, accounting for 0%, 27.3% and 57.8% of all coal-resource-exhausted cities, respectively. Furthermore, the number of four-star cities in the second stage increased by 3.2% over the first stage. The number of three-star cities was three, seven and nine in 2005, 2010 and 2015, respectively, while the increased number of four-star cities in the second stage was 3.0% lower than that in the first stage. According to this algorithm, the number of two-star cities in the second stage decreased by 30.2%, which was lower than that in the first stage. The number of one-star cities in the second stage decreased by 78.8% compared with the first stage. Therefore, the overall improvement in the second stage was faster than that in the first stage, and this trend will be more obvious in coal-resource-exhausted cities with a lower number of stars.The obstacle degree model is a quantitative analysis model to evaluate the specific obstacle factors affecting the measured indicators. The previous obstacle degree model tended to measure the obstacle degree of specific time and location nodes in a single index, but could not measure the comprehensive obstacle degree of a single index. Based on the factor contribution degree and comprehensive deviation degree of the indicators, the obstacle degree model in this study extends the significance of the standardized value of a single index and takes the expectations of all time points and location samples under a single index as the comprehensive value The obstacle degree of each obstacle factor affecting the green development of 33 coal-resource-exhausted cities in China can be calculated by the improved obstacle degree model and each obstacle factor is represented by a corresponding code. The results are shown in As can be seen from In terms of specific obstacle factors, among the 23 obstacle factors, 4 had an obstacle degree of more than 8% .Seven obstacle factors were close to or more than 6% and less than 8%  and one was 4%-5% .Eleven obstacle factors were less than 3% .Among C11, financial expenditure, the obstacle degree of the proportion of science and technology expenditure was the highest, at close to 10%. The factors with a high barrier degree and greater than 8% were the comprehensive utilization rate of C4, general industrial solid waste, the possession of public cars (electric vehicles) per 10,000 persons in C18 and the greening coverage rate in a built-up area, C7. In addition, there were other factors with high barriers, such as the collection of books in public libraries per 100 persons in C19, the number of doctors per 10,000 persons in C20, the proportion of employed population in primary industry in C21, the proportion of education expenditure in financial expenditure in C12, the per capita GDP of C8, the proportion of output value of the tertiary industry in C15, the average annual salary of employees in C10, etc.In the criterion layer index of resources and the environment (21.59%), the obstacle degree of C1, emission of GRP industrial dust, was 0.20%; C2, emission of GRP industrial sulfur dioxide, was 1.27%; C3, emission of GRP industrial wastewater, was 0.29%; C5, centralized processing rate of sewage treatment plant, was 2.06%; and C6, harmless treatment rate of household trash, was 0.72%—all less than 3%. This has little impact on urban green development. In the criterion layer index of economic development (38.88%), the obstacle degree of C14, proportion of output value of the secondary industry, was 2.02%; C15, the proportion of output value of the tertiary industry, was 6.16, which means the impact on green development of the tertiary industry was larger than the secondary industry. Furthermore, in the criterion layer index of population and social welfare (39.53%), the obstacle degree of C16, population density, was up to 4.45%; C17, natural growth rate of population, was 2.52%, which shows that the population density has a great impact on urban development. This study has found that economic development and the population’s social welfare have greater impacts on urban sustainable development.With regard to the characteristics of regions or specific obstacle factors between different groups of obstacle degrees, the characteristics of certain regional or specific obstacles exist between different groups or regions. For instance, due to geographical location and climatic conditions, the economy of coal-resource-exhausted cities in the western region of China is not as well developed as that of the eastern coastal groups, and social welfare  and economic development  are the key factors affecting green development in these groups.First, the green development of coal-resource-exhausted cities in China has improved steadily and rapidly in the early stages. Through the calculation and analysis of the scores of the green development level, and the extreme value and mean value of coal-resource-exhausted cities in 2005, 2010 and 2015, it was shown that the level of green development showed a steady and upward trend from 2005 to 2015. Through the star-standard division and statistical analysis of the data, it was found that the level of green development from 2005 to 2010 was faster than that from 2010 to 2015, that is, the level increase in green development of coal-resource-exhausted cities in the first stage of the early stage was more than that in the second stage of the later stage; this trend was more obvious in coal-resource-exhausted cities with a lower number of stars.Second, Zibo, Lanzhou and other cities with high levels of green development showed relatively stable green development. Through the calculation of the stability coefficient of data from the above three years, it was found that the stability index of the top five cities was 0.3, indicating that their ranking was relatively stable. The life cycle of resource-based cities, different effects of transformations on cities at different development stages and the evolution of the development stages of resource-based cities can directly reflect the changing process of urban development.Third, the level of green development of most cities has improved; however, some cities have declined. Zibo was the city with the highest average green development and Laibin was the lowest. Wuhai made the fastest progress and Hegang made the slowest progress. By calculating the average level of green development in the three years, it was found that the three cities with the highest average level of green development were Zibo, Lanzhou and Xuzhou. The three cities with the lowest average level of green development were Laibin, Chaoyang and Chengde. During 11 years, the score ranking of green development in Wuhai increased by 23 places, and was 52% higher than that in 2005. Hegang, the city with the slowest progress, fell 20 places. In addition, it is worth noting that Baotou and Jingzhou regressed in green development and the level of green development in 2015 was lower than that in 2010.Fourth, the transformation of 33 coal-resource-exhausted cities demonstrated certain regional characteristics. The green transformation and development of coal-resource-exhausted cities in China were mainly government-oriented. As the coal-resource-exhausted cities in the same province or region receive the same policy, plans for green transformation and development will be similar in the same region. Each coal-resource-exhausted city has different advantageous resources, which leads to different resource bases for green transformation, for example, Zaozhuang has built the Taierzhuang ancient town, which relies on canal culture, red cultural resources, etc., promoting city transformation through culture and tourism. Zibo, which is in the same province, also has rich cultural and tourism resources; however, the promotion of city transformation through culture and tourism is not appropriate here. Instead, high-value-added industries were developed, and now it has basically formed an industrial system dominated by four industries: building and new materials, medicine and fine chemicals, machinery manufacturing, and textiles and clothing. Hence, according to the regional characteristics, the results show that regional bias exists in resource-exhausted cities, and there are also biases in the same region.Fifth, low financial expenditure on science and technology has become the primary reason for hindering green development. Based on the analysis of the obstacle degree, the obstacle degree of the proportion of science and technology expenditure in terms of total financial expenditure was close to 10%. There are also three factors with a high barrier degree that cannot be ignored: the comprehensive utilization rate of general industrial solid waste, the ownership of public buses (electric vehicles) per 10,000 persons and the greening coverage rate of a built-up area, which are reflected in the utilization of industrial waste, social welfare and urban greening, respectively. In addition, the factors with a high barrier degree also involve many aspects, such as medical treatment, agricultural population, education, regional GDP, tertiary industry, wages and salaries, etc. Moreover, ecological remediation is an important task in the economic transformation of coal-resource-exhausted cities. In this research, we studied barriers to green development in coal-resource-exhausted cities. It was found that factors related to the population’s social welfare and economic development have a great impact on the green development of resource-exhausted cities, and the low proportion of financial investment into science and technology is the main cause. The research shows that continuously increasing financial investment in the areas of scientific and technological innovation and establishing a diversified mechanism can promote the transformation of new and old kinetic energy, fundamentally improve the ecological environment in coal-resource-exhausted cities and achieve green development.Compared with previous research, our study contributes to the green sustainable development of resource-exhausted cities in the following ways:First, we explore a new research perspective  to focus on resource-exhausted cities that are facing resource depletion, environmental problems, the recession of industry and imbalanced unemployment, etc. . Based oSecond, this study has filled the research gap in terms of the theory of green sustainable development in resource-exhausted cities and improved it further by a series of empirical studies and analyses, providing relative research results. It has been demonstrated that green development has a positive impact on improving social and ecological environmental problems ,81,82. CThird, this study adopted both the qualitative and quantitative analysis method. The qualitative analysis method was used for the selection of indicators and selected indicators scientifically and reasonably. The quantitative analysis method was adopted for index weighting to avoid artificial bias and subjective factors affecting the objectivity of index weighting. Therefore, with the goal of green and sustainable development, this study builds a three-level index system by using development analysis tools and a principal component extraction method.Finally, based on the results of our empirical analysis, this study discusses the life cycle of resource-based cities ,84 and tAccording to the actual evaluation situation of the green development of coal-resource-exhausted cities and the discussion regarding the life cycle of resource-based cities combined with the conclusions regarding the obstacle degree analysis, the following recommendations are drawn:First, science and technology are core components of green development. The biggest obstacle for coal-resource-exhausted cities in achieving green development is the lack of scientific and technological financial expenditure . On one Second, prosperity is associated with improving welfare. Achieving green development not only “enriches the country” but also “enriches the people”. On the basis of green production, improving welfare is another important part of green development. The measures include establishing and improving public transport systems and the use of new energy. Moreover, the government should also increase investment in public healthcare and education, focusing on increasing the number of community doctors and reducing the medical pressures on large hospitals.The third conclusion concerns environmental protection and ecological restoration. The ecological environment of coal-resource-exhausted cities is generally poor . TherefoBased on the characteristics of typical resource-exhausted cities, this study tried to reveal the impacts of different regions, as well as reflect regional variations, on urban transformation. We further analyzed the obstacle factors and obstacle degree of green development on resource-exhausted cities by empirical analysis. Furthermore, the stage characteristics of the evolution and life cycle of coal-resource-exhausted cities are also discussed in this study in order to explore the ways of green sustainable development. There are several limitations to be noted that also provide directions for future studies.First, affected by the limitations of data acquisition, this study only evaluated the green development level of 33 coal-resource-exhausted cities, and in a future study, we will continue to track the latest data to continuously evaluate the green development level of more cities with significant regional characteristics, as well as the impact of the barriers in different regions on green sustainable development.Second, principal component analysis was mainly adopted in this study, which can ensure that the weight of each index completely depends on the objective information during the calculation process, avoiding the deviation caused by subjective factors. Meanwhile, on the premise of avoiding the loss of information, the high-dimensional index vector was reduced, simplifying the multivariate panel data and reducing the workload. However, this method also has some problems. The principal component analysis is carried out from the correlation matrix of the sample, that is, the original data should be standardized, but the standardization eliminates the variation degree of each variable while eliminating the dimension or order of magnitude. Moreover, extracting the variance contribution rate of each principal component as the weight to calculate a comprehensive score will have certain subjective components. Hence, the entropy method, integrated comprehensive evaluation, principal component clustering method, etc., may be used to improve this study in the future.Third, during the construction of the evaluation index system of green development for coal-resource-exhausted cities, indicators concerning environmental pollution will inevitably be involved, which are also the core factors affecting the green transformation and development. In the process of the field investigation, the data were from 33 cities over a 10-year period, which may mean there are missing or fuzzy data. This makes it more difficult to obtain objective first-hand data. In a future study, according to the development and improvement of government, enterprise and public awareness of green development, each index layer of the evaluation index system of green development could be appropriately adjusted for a more comprehensive and systematic analysis.In addition, according to the degree of green development of cities, the development stages of cities can be classified into several types; however, this study is mainly focused on resource-exhausted cities, ignoring both growing and mature resource-based cities. The core of urban construction is always how to achieve the green sustainable development. Thus, a future study will further expand the green development levels of mature resource-based cities and growing resource-based cities.On the basis of analyzing the characteristics of coal-resource-exhausted cities, this study builds an evaluation index system of green development that can provide a reference for the green transformation and sustainable development of resource-exhausted cities. Based on theoretical reviews, empirical analysis and discussion, the following conclusion can be drawn from this study:Urban construction should move towards a composite system of “production, ecology, life”. Improving the ecological environment, promoting green low-carbon industries and realizing circular development will become the pursuit of green urban development. Building a green city is a systematic project, gradually realizing the prosperity of green production, the benefits of sharing and symbiosis with green living, reaching a consensus and reconciliation between humans and nature . 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Based on data from 64 resource-based cities in China from 2010 to 2019, the efficiency of green innovation is evaluated by using the super-efficiency SBM Model with undesired outputs, while influencing factors of green innovation efficiency are analyzed by the spatial Durbin model. The results are as follows. First, as for the efficiency evaluation, the average green innovation efficiency in 62 resource-based cities from 2010 to 2019 is only 0.5689, while the green innovation efficiency of declining cities is the highest, and the growth type is the lowest in the comprehensive planning cities. Second, based on spatial self-correlation in resource-based cities, the government support, and the influencing factors including the industrial structure and economic development, have positive impacts, while the environmental regulations and opening to the outside world will inhibit the urban green innovation. Therefore, to enhance the green innovation efficiency in resource-based cities, some suggestions include formulating differentiated development strategies, forming regional cooperation mechanisms, increasing government scientific and technological support, determining the reasonable intensity of environmental regulations, setting entry barriers for polluting enterprises, and optimizing industrial structure. Green innovation is at the core of the transformation of traditional cities to green cities, and the green innovation efficiency is an important performance for cities to maintain a sustainable economic competitive advantage. Green innovation in cities is essentially an action to transform the economic development of cities from the traditional mode to the combined mode of environmental friendliness and the efficient use of resources. With green friendliness as the start of the path and scientific and technological innovation as the driving force and key point, the green innovation efficiency is the ultimate manifestation of this action. Resource-based cities mainly refer to a special type of city that rises from resource extraction and relies mainly on resource-based industries to support their economic development . In 2013Enhancing the green innovation capacity of resource-based cities can help resolve the multiple crises which are being faced by the development of resource-based cities and promote their sustainable development . To achiThis paper evaluates the efficiency and influencing factors of green innovation in resource-based cities using the super-efficiency SBM model with a non-expected output, Moran’s I index, and the spatial econometric model, respectively, with the data of 64 re-source-based cities in China from 2010 to 2019 as the samples. The research in this paper finds that (1) the mean value of the green innovation efficiency of resource-based cities is low between 2010 and 2019, which is only 0.5689, among which the green innovation efficiency of declining cities is the highest and the lowest of growing cities among the integrated types of planning. (2) The green innovation efficiency of resource-based cities is spatially autocorrelated, and government support, industrial structure upgrading, and economic development will promote the green innovation efficiency of resource-based cities, while environmental regulation and opening to the outside world will inhibit the development of urban green innovation.The contribution of this paper is that (1) current resource-based city-related studies focus on directly measuring the transformation efficiency of resource-based cities while lacking the exploration of the drivers of transformation, i.e., the green innovation efficiency ,5, thus The rest of this paper is organized as follows: the second part is a literature review, the third part is a research design of the green innovation in resource-based cities, the fourth part is an evaluation of the efficiency of the green innovation in resource-based cities, the fifth part is an analysis of the influencing factors of the green innovation efficiency in resource-based cities, and finally, we present the conclusions and recommendations.The definition of green technology, first proposed by Ernest in 1994, is a collective term for the goods, technologies, and processes that eliminate or reduce pollution while saving resources . Green iIn terms of a green innovation efficiency evaluation, most of the existing studies on a green innovation efficiency evaluation follow the traditional stochastic frontier analysis method and data envelopment analysis method. For example, Yixin Zhang and Xiumei Lin 2015) combined the DEA model with the Malmquist index method to measure the inter-provincial green innovation efficiency in China from both the static and dynamic aspects . Based o combinedAnalyzed in terms of the relevant aspects of the green innovation efficiency, SMEs are a typical indicator that take into account both the firm and environmental characteristics and are therefore subject to the influence of multiple factors. It is generally determined that the economic level can lead to more new factors, which in turn promotes an efficient technological innovation in SMEs . An enviIn summary, domestic and foreign scholars have conducted a certain amount of research around the issues of the green innovation efficiency evaluation and the green innovation efficiency impact factors and have achieved results of a great significance which provide a rich theoretical foundation for the research of this paper, but it is not difficult to find that there is less research on the green innovation efficiency with resource-based cities as the evaluation object, and resource-based cities are facing a critical period of transformation development driven by green innovation. Although the existing literature is relatively comprehensive in terms of the index system of the green innovation efficiency evaluation, it does not reflect the characteristics of the innovation development of resource-based cities and is not suitable for the measurement and evaluation of the green innovation efficiency of resource-based cities directly. Based on this, this paper measures and analyzes the green innovation efficiency of resource-based cities and its influencing factors based on the efficiency perspective, using the relevant data of 64 prefecture-level resource-based cities from 2010–2019, and applying the super-efficiency SBM model considering the non-expected output, to provide a scientific basis for decision making to improve the green innovation efficiency of Chinese resource-based cities and realize the sustainable development of Chinese resource-based cities.According to the concept in the National Sustainable Development Plan for Resource-based Cities (2013–2020) (hereinafter referred to as the Plan) issued by the General Office of the State Council in 2013, there are currently a total of 262 resource-based cities in China, of which 116 are prefecture-level cities. Given that the prefecture-level cities in the resource category often possess the characteristics of long development history and large scale, their requirements for sustainable urban development are particularly urgent. Therefore, in this paper, 64 prefecture-level cities across China are selected as the main research subjects according to the availability and completeness of research data. Meanwhile, according to the content in the Plan, these 64 resource-based cities are divided into four categories: Growth type, Mature type, Decline type, and Regeneration type. The details are shown in Based on the scope and availability of the data, this paper selected information from 64 resource-based cities during the period from 2010 to 2019 as the database for the study. Most of the data were obtained from the China City Statistical Yearbook, EPS database, and the websites of the Department of Science and Technology of each city’s statistical yearbook. Among them, some incomplete data were compensated by the linear interpolation method.In this paper, the green innovation efficiency of resource-based cities is assessed and analyzed by an over-efficient SBM model that considers non-desired outputs. Compared with the traditional DEA model, the SBM model analyzes the slack changes in the objective function and deals with the slack relationship with input–output variables, and also examines the effect of non-desired outputs on efficiency. However, if the effective value of all the efficient decision units measured by the SBM model is one, it will lead to unrecognized value differences within all the efficient decision units, resulting in an error in the final decision. The super-efficient SBM model, on the other hand, can reclassify all the effective units with an efficiency value of one, so that the comparison of all the effective decision units can be performed, enhancing the accuracy of the decision analysis. The formula of the super-efficient SBM model considering the non-desired outputs is as follows.In Equations (1) and (2), In this paper, we used the global Moran’s I index analysis to test the spatial autocorrelation of the green innovation efficiency in resource-based cities. The global Moran’s I index will help to verify the spatial development pattern within the study area, expressing the general nature of the variation in the mean value of a function over the study time horizon. Its expression is:In Equation (3): I denotes the Moran index andIn contrast to the traditional regression methods, spatial measures take into account the complex spatial correlation and spatial dependence of the sample  [In Equation (4), the 2 emissions are used as the measures of the unintended outputs, according to the practice of Li Jian et al. (2019) [In constructing the input-output index system for the green innovation efficiency evaluation of resource-based cities, we draw on previous research results to fully consider the environmental factors and innovation factors, while taking into account the scientificity of the relevant indicators and the availability of data for resource-based cities. In terms of the input indicators, previous studies generally consider that the input of an activity factor mainly covers the three levels of the labor input, capital input, and energy input ,26. Amon. (2019) . The detIn this paper, the super-efficient SBM model considering the non-desired output is used to measure and obtain the mean value of the green innovation efficiency in resource-based cities from 2010–2019, as shown in Longitudinally, it can be seen from To further analyze the differences in the green innovation efficiency of different resource-based cities, this paper divides each resource-based city into different groups according to the comprehensive planning classification and calculates the mean value of the green innovation efficiency of resource-based cities within each group, respectively, and the results are reflected in The sample classification of 64 resource-based cities in this paper covers four comprehensive classification types of resource-based city planning: the growth type, mature type, decline type, and regeneration type. As can be seen from In addition, from the time series, the green innovation efficiency value of the four types of resource-based cities in the integrated planning classification has a clear upward trend in 2014, and the declining resource cities reached the highest point of the average efficiency at 0.9779 in 2019, only 2.21% away from the production frontier. Mature resource cities reached the highest efficiency value of 0.6791 in 2017, but there is still much room for improvement from the effective state. Regenerative resource cities also maintained a relatively stable growth in 2014. Growing resource cities reached a growth of 123.01% in 2015–2016, which, as the analysis shows, is caused by the fact that Songyuan City has greatly reduced the discharge of their industrial wastewater and increased their green output after 2015.The Plan calls for categorizing and guiding the scientific development of various types of cities, including supporting the transformation and development of declining cities, guiding the innovative development of regenerating cities, promoting the leapfrog development of mature cities, and regulating the orderly development of growing cities. From this, it can be seen that the planning objectives are not the same for the development of different types of resource-based cities in 2013. Due to the gradual depletion of resources, declining cities are in urgent need of transformation and development, while regenerating cities are the focus of the transformation of resource-based cities, both of which need to be driven and led by innovation and pay attention to ecological and environmental issues in the process of economic development. In 2008–2009, the State Council announced 44 resource-depleted cities to help them achieve economic transformation and development, and the transformation of declining resource cities was carried out earlier and achieved certain results, forming a good foundation that made their green innovation efficiency from 2010–2019 higher than that of other types of cities. The green innovation efficiency of each type of resource city measured by the comprehensive classification of the Plan indicates that the declining resource cities currently meet the development requirements of the Plan in terms of green innovation, while the regenerative resource cities face greater difficulties and need to develop suitable strategies to enhance their green innovation level.(1)Environmental regulation intensity(er). Traditional management thinking suggests that environmental pollution controls raise the cost of controlling emissions and reduce the quality and competitiveness of a firm’s products. The Porter hypothesis states that tough and rational environmental policies can partially or fully offset these effects by encouraging firms to innovate, which in the long run helps to achieve a win-win situation of environmental improvement and competitive innovation. Therefore, the impact of environmental regulation on the efficiency of green technology innovation is due to a squeezing effect on technology innovation due to the increase in a firms’ production costs and an incentive effect due to the incentive to innovate technology to reduce costs. Referring to the practice of Cao Xia et al. (2015), this study selects “three waste utilization rates” as a proxy variable . Accordi(2)The level of government support (tech). The government funding support policy specifies the quantity and quality of green technology innovation, which in turn promotes the development of green innovation . Technol(3)The level of industrial structure (ind). The development of a secondary industry has a great role in promoting the growth of the national economy, but it brings a high energy consumption industry and a lot of environmental pollution at the same time, such as steel, crude oil chemical products, metal machinery processing, and other excess product industries. While the enhancement of green innovation depends more on the reduction in environmental resources, the development of the tertiary industry with a low input, resource recycling, and a high pollution reduction, which drives the development of economic efficiency with the enhancement of the manufacturing structure, also reduces pollutant emissions and energy costs, thus promoting the enhancement of the green innovation efficiency. In this study, the proportion of the regional tertiary industry gross industrial output value to GDP is selected as a proxy for the level of industrial structure.(4)The level of external openness (fdi). The “pollution paradise theory” believes that pollution-intensive industries in other developed countries prefer to set up companies in developing countries or places with correspondingly lower technical standards for environmental protection, which is beneficial to local employment solutions but not to the optimization of the industrial structure; while the “double gap” theory states that regions with a higher level of external opening can be the first to master leading foreign technologies. Therefore, the technology spillover effect and pollution diffusion effect of opening up to the outside world cause opposite effects on the green innovation of the whole city. In this study, the ratio of the amount of actual foreign investment utilized in each region to the GDP of that region is chosen as a proxy variable for the level of foreign openness.(5)The level of economic development (dev). On the one hand, economic development brings financial security to the development of science and technology innovation, the improvement of the environmental resource structure, and the improvement of the urban environmental efficiency; on the other hand, since the level of economic and social development often reflects the highest standard of living of the inhabitants of a region, and the growth of average social income contributes to the preference for a better state of life, the choice of the inhabitants is also the main external driver of green efficiency improvement. Where the market economy is more mature, people are more sensitive to the environmental pollution emissions implied in traditional products and therefore turn to invest in environmental protection products ,32, but First, based on GeoDa software to measure the Moran’s I index of 64 resource-based cities from 2010–2019, the geographic matrix was selected to test the spatial correlation of the green innovation efficiency in resource-based cities, and the cities that were not adjacent to the map were linked to the nearest resource-based cities to further correct the adjacency weight matrix, and the results are shown in In The statistical results of Moran’s I index show that there is a spatial correlation between the green innovation efficiency, indicating that it is feasible to study the factors influencing the green innovation efficiency through a spatial econometric model. Econometric tests were conducted for the sample of 64 resource-based cities as a whole and their four categories of cities were classified according to the different types, and the results are shown in As shown in (1)There is a significant negative correlation between the intensity of the environmental regulations and the green innovation efficiency. In general, the standardized regression coefficient of the environmental regulation intensity measured by the “three waste” treatment rate is −0.016 and it passes the 1% significance test, indicating that the environmental regulation inhibits the improvement of the green innovation efficiency in resource-based cities. In terms of the classification of resource-based cities, the four categories of resource-based cities, namely mature, declining, regenerating, and growing cities, all have a significant negative relationship with the green innovation efficiency, with mature resource-based cities having the highest degree of influence. It can be seen that under the influence of government-led environmental protection policies, the production cost of enterprises subsequently increases, and its squeezing effect on technological innovation will make the green innovation efficiency of resource-based cities negatively affected. Therefore, for the use of environmental regulations in resource-based cities, a reasonable and bottom-up measure should be preferred to carry out, and only when environmental regulations have a positive incentive effect on technological innovation, if such a system is coordinated and effective.(2)There is a significant positive relationship between the level of government support and the green innovation efficiency. The overall regression coefficient of government support on the green innovation efficiency is 0.027 and it passes the 1% significance test, that is, for every 1% increase in government support, the green innovation efficiency of resource-based cities will increase by 0.027%. In terms of classification, mature, declining, regenerative, and growing resource-based cities all show a significant positive relationship with the green innovation efficiency, among which declining resource-based cities have the highest influence. The degree of green innovation is the highest. The government reduces the technological innovation risk of enterprises through direct financial support and other means and makes resource-based cities form a good innovation atmosphere to a certain extent, which motivates enterprises to carry out innovation activities and weakens the cost squeeze effect of green innovation.(3)There is a significant positive correlation between the level of industrial structure and the green innovation efficiency. Overall, the value of the regression coefficient of the industrial structure level expressed as the proportion of the regional gross industrial output value of the tertiary industry to GDP is 0.139 and it passes the 1% significance test, that is, for every 1% increase in the industrial structure level, the green innovation efficiency of resource-based cities will increase by 0.139%. From the classification point of view, mature resource-based cities, declining cities, regenerating cities, and growing cities have a significant negative relationship with green innovation, with mature resource-based cities having the highest degree of influence. This indicates that the more the industrial structure of resource-based cities tends to be reasonable, the more it is conducive to the improvement of ecological environmental protection and the technological innovation efficiency level, and the development of the tertiary industry for resource-based cities reduces both pollutant emissions and the pressure of resource extraction, so it is undoubtedly correct for resource-based cities to pay attention to the development of the tertiary industry for the sustainable development of resource-based cities.(4)There is a significant negative correlation between the level of foreign openness and the green innovation efficiency. Overall, the regression coefficient value of the level of foreign openness on the green innovation efficiency is −0.101 and it passes the 1% significance test, which means that enhancing the level of foreign investment utilization and expanding the openness of cities will, to a certain extent, reduce the green innovation efficiency of resource-based cities instead. In terms of classification, mature, declining, regenerating, and growing resource-based cities are all significantly and negatively correlated with the green innovation efficiency, with regenerating resource cities having the highest degree of influence. Foreign investment in resource cities has a squeezing effect on the local market, which not only squeezes the investment space of local industries but also leads to the phenomenon of “pollution paradise”, where more pollution-intensive enterprises settle in resource cities and cause pollution diffusion, which harms the local ecological environment.(5)There is a significant positive relationship between the level of urban economic development and the green innovation efficiency. On the whole, the regression coefficient value of the city’s economic development level is 0.082, and it passes the 1% significance test. A 1% increase in the city’s economic development level will increase the green innovation efficiency by about 0.082%; in terms of classification, mature, declining, regenerative, and growing resource-based cities all have a significant negative relationship with the green innovation efficiency, among which renewable resource-based cities have the highest degree of influence. Having a higher level of economic development in resource-based cities will not only bring financial security to local scientific and technological innovation development, environmental resource structure improvement, and upgrading, but also transform residents’ living needs, making them more inclined to choose green products and improve their requirements for a living environment, both of which force local enterprises to choose green innovation and thus improve the city’s green innovation level.In this paper, using the panel data of 64 resource-based cities in China from 2010–2019 as a sample, we measured the green innovation efficiency of each resource-based city using the super-efficient SBM model with a non-expected output, and then conducted a classification study on the efficiency measurement results, and tested the spatial autocorrelation of resource-based cities using the global Moran’s I index, based on which the spatial Durbin model was used to test and analyze the factors influencing the green innovation efficiency of resource-based cities. The following conclusions were obtained.(1)The evaluation of green innovation efficiency in resource-based cities. As a whole, the mean value of the green innovation efficiency of resource-based cities fluctuates and decreases from 2010 to 2014, and from 2014 to 2019, it shows a gentle upward trend, where 2014 is the turning point of the change in the mean value of the efficiency. Only nine cities, such as Laiwu City, among resource-based cities are at the production frontier surface, while all other cities have problems such as redundant factor inputs, an insufficient output efficiency, and the excessive output of negative environmental externalities during the sample period, and there is also a more obvious variability among the green innovation efficiency of each resource-based city. From the results of the analysis of the difference in the planning comprehensive classification, from 2010 to 2019, the four types of resource-based cities are at a low level from 2010 to 2019, and their green innovation efficiency is the decline type, mature type, regeneration type, and growth type in the order from high to low, among which the green innovation efficiency of declining resource-based cities is always at the highest level among all categories.(2)The influencing factors of the green innovation efficiency in resource-based cities. From the green factors, there is a significant negative correlation between the intensity of the environmental regulation and the green innovation efficiency, indicating that the environmental regulation inhibits the improvement of the green innovation efficiency in resource-based cities, and how to design an effective environmental protection system and give full play to the incentive effect of environmental regulation is an important choice for government departments. From the innovation factors, there is a significant positive correlation between government support and the green innovation efficiency, and improving government support is an effective way to improve the green innovation efficiency in resource-based cities. In terms of the comprehensive factors, the level of economic development of cities has a significant role in promoting the green innovation efficiency, indicating that the economic and scientific strength brought by economic development provides a strong guarantee for the improvement of the green innovation efficiency in resource-based cities. There is a significant negative correlation between the level of external opening and the green innovation efficiency, indicating that most resource-based cities in China at the present stage have become the transfer place of foreign low-level industries, which harms local development. There is a significant positive correlation between the level of industrial structure and green innovation efficiency, so the optimization and upgrading of industrial structure is an effective way to improve the green innovation efficiency of resource-based cities.(1)Develop differentiated development strategies. Based on the characteristics of resource-based cities, we will develop differentiated strategies suitable for green development in each region. For example, the mature, regenerative, declining, and growing resource-based cities, as defined in the Plan, are different in terms of their resource endowment and ecological environment, and the degree and direction of their green innovation are different and needs to be improved. For cities that are seriously lagging behind in the green innovation efficiency, including regenerative and growing resource cities, we should focus our attention and support on understanding and solving the obstacles to green development and innovation development in these areas, reasonably planning the industrial structure, improving the access mechanism for high energy consumption and high pollution enterprises, and encouraging and supporting the development of clean and sustainable industries.(2)Formation of a regional cooperation mechanism. Since green innovation has a spillover effect, the formation of a perfect regional cooperation and mutual assistance mechanism is necessary to enhance the level of green innovation and reduce the differences in green innovation in resource-based cities. To address the problem of resource mismatch, an organization with complementary resources and mutually beneficial policies can be established for coordination, and bilateral or multi-governmental cooperation can be actively carried out. Drawing on the development experience of cities with a high efficiency in green innovation, strengthening the flow of outstanding talents between regions, and enhance their green innovation capacity breaks the local authorities with administrative regions as boundaries, establishes a long-term mechanism for coordinated regional development, and gradually reduces the gap between cities.(3)Strengthen the government’s scientific and technological support and set the intensity of environmental regulations scientifically and reasonably. Some examples of this are: continuing to enhance the policy support capacity, formulating appropriate environmental regulation measures, releasing the innovation vitality of enterprises, improving the innovation carrier capacity and leading role of SMEs, encouraging SMEs to increase research investment and set up research centers, guiding SMEs to carry out basic and cutting-edge R&D, and investing more funds in absorbing human resources and improving the innovation manufacturing efficiency.(4)Set the market access threshold for polluting enterprises and optimize the level of industrial structure. The local government will introduce corresponding policy measures guided by green innovative economic development and fully support the development of green industries in the city, promote high-tech industrial enterprises and green and environmentally friendly product enterprises in resource-based cities, and establish a strict review system and supervision for the entry of polluting enterprises, to achieve a green and sustainable development path for the city.