Public-Private Partnerships (PPPs) are increasingly used globally for infrastructure and public service delivery, offering advantages such as improved construction, efficiency, and risk transfer. However, concerns exist regarding value for money and overreliance on PPPs, potentially leading to increased debt and inefficiencies. PPPs are not a universal solution; their suitability varies spatially, with some regions and sectors less suited than others, especially those lacking stable cash flows. This spatial heterogeneity is a key characteristic, evidenced by global and national disparities in PPP project distribution. Factors influencing PPP success include urbanization, economic development, governance, regulations, and actor networks. Therefore, a model assessing PPP suitability in diverse regions is crucial, particularly in a geographically diverse country like China. This study focuses on the city-level suitability of PPPs, contrasting it with project-specific suitability analysis, which is a separate area of research. The aim is to develop a quantitative model to evaluate the suitability of adopting PPPs in Chinese prefecture-level cities, enriching global PPP governance understanding, providing a scientific basis for sustainable PPP development in China, and offering valuable insights for other developing countries considering PPP adoption.

Public-Private Partnerships (PPPs) are long-term collaborations between governments and private sectors for infrastructure and public service delivery, encompassing various models like BOT, PFI, and DBFO. While offering potential benefits, their applicability varies across projects. Many studies have focused on individual project suitability, analyzing feasibility and aligning with procurement frameworks, which is distinct from the city-level suitability examined in this study. The geography of PPPs is understudied despite the significant spatial differences in implementation and outcomes. PPPs are influenced by urbanization, territory factors, natural environment, social structures, economic development, and culture. Existing research on city-level PPP suitability is scarce, often focusing on broader national-level evaluations of legal frameworks, government units, and financial markets rather than city-specific contexts. This study innovatively assesses PPP suitability within a city context, using China's extensive PPP experience as a case study, particularly the significant growth since 2014 and the subsequent government reviews and rectifications that provide a rich data landscape. The study bridges a gap in research, addressing the need for nuanced understanding of city-level PPP suitability, particularly in developing countries.

This study employs a target-rule-indicator framework to construct a PPP suitability assessment model. The target is to evaluate a city's suitability for PPPs. The rules considered are urban development needs, financial security, government credit, regulatory capacity, local government efficiency, and urban development characteristics. The indicators used are GDP, urbanization rate, infrastructure investment (for development needs), government-business relationship (local government efficiency), financial revenue, financial self-sufficiency (fiscal capacity), government credit, number of local PPP projects, number of local PPP demonstration projects (government regulatory capacity), and environment and social sustainability (urban development characteristics). The research design involves three stages. First, indicator selection based on the target-rule-indicator framework and literature review. Second, using the entropy weight method to assign weights to indicators based on their variation and information content. This method objectively weighs indicators based on their dispersion, prioritizing indicators with larger variations and reflecting information intensity. Third, applying the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method to rank cities based on their suitability scores. TOPSIS utilizes distances from positive and negative ideal solutions to determine suitability. Data for the 287 prefecture-level cities in China was gathered from various open databases such as the China Urban Statistical Yearbook, the PPP project database of the Ministry of Finance of China, CreditChina, and reports from the National Institute of Development and Strategy at Renmin University of China. Data normalization was done using equations to standardize indicators with positive and negative effects (bigger is better or smaller is better). The normalized matrix and the entropy weights were combined to form the final evaluation matrix used for TOPSIS.

The entropy model determined weights for the 11 indicators, with GDP and infrastructure investment having the highest weights (0.23214 and 0.15049 respectively). The TOPSIS method calculated a distance value (T) for each city, indicating its suitability for PPPs; a higher T value suggests greater suitability. Using the natural breakpoint method, the cities were categorized into four levels: high suitability (T > 0.3459), medium suitability (0.2229 ≤ T ≤ 0.3458), low suitability (0.1446 ≤ T ≤ 0.2228), and unsuitability (T < 0.1445). Significant spatial heterogeneity was observed. Provincial capitals and economically developed cities, particularly in the eastern coastal region, showed high suitability. Economic development was a major factor determining suitability, with 219 cities showing low or unsuitability (76%), indicating limited large-scale PPP applicability in the current context. High-suitability cities (18 cities) were mostly national or regional central cities, characterized by developed economies, infrastructure, high government credit and regulatory capacity, and strong government-business relationships. Medium-suitability cities (50 cities) comprised provincial capitals or regional central cities, demonstrating varying levels of economic development and PPP experience. Low suitability cities (81 cities) mainly included inland cities with mid-to-upper level economic indicators but lacking PPP experience and having weak government services and regulations. Unsuitable cities (138 cities, 48%) are concentrated in western and northeastern China, showing underdeveloped economies, low fiscal revenue, low urbanization rates, lagging infrastructure, weak governance, and limited PPP project demand.

The findings support the idea that PPPs are not universally suitable and highlight the importance of considering spatial differences. Economic and financial factors were crucial, consistent with previous studies, with developed cities having more assured project returns. However, the study cautions against assuming that less-developed cities are unsuitable; appropriate regulations and project management are needed to leverage PPPs for economic growth and infrastructure development. The spatial variation necessitates targeted policies: high suitability cities should be encouraged to utilize PPPs, while those with medium suitability should adopt cautious approaches. Cities with low or no suitability require capacity building and limited pilot projects before widespread adoption. PPPs should be viewed as a complementary model, used selectively and strategically.

This study presents a country-based assessment model for urban PPP suitability, providing a scientific basis for sustainable PPP development in China. The research enriches the understanding of PPP governance and addresses the challenges faced by China's PPP sector, offering insights for other developing countries. While the model provides valuable insights, limitations exist in indicator selection and data availability. Future studies should refine the indicators, explore dynamic interactions between factors influencing PPP success, and conduct comparative and longitudinal studies to further enhance the model's applicability and robustness. Policy recommendations include a nuanced approach to PPP implementation, adhering to standardized legal frameworks and improving transparency and accountability.

The study's limitations include potential incompleteness in indicator selection, data availability constraints influencing the accuracy of results, and uncertain applicability to other countries due to data requirements. Future research should address these limitations by expanding the indicator set, considering expert input, conducting cross-country comparisons, and exploring the dynamic interplay of factors influencing PPP success.