# Mathematical Models for Analysis the Impact of Institutional Quality and Multilateral Financial Institutions on the Success of Green PPP Projects

Authors: Wu-Yan Li; Ruo-Yu Chen; Ming Ma
Journal: Journal of Function Spaces
 (2022)

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## Abstract

Green PPP projects are of great significance to green infrastructure construction and green economic development. This study analyzes 552 Green PPP projects related to sewage treatment and clean energy in African countries during 2005-2019 to determine how institution quality and multilateral financial institutions influence Green PPP projects’ success. We find that (1) successful (failed) Green PPP projects experience increase (decrease) new Green PPP projects’ success rate. (2) Higher private capital risk-taking degree increases the likelihood of failure. (3) Institution quality affects project success through a mixed effect on private capital’s risk-taking degree:rule of law, government effectiveness, control of corruption, and regulatory quality positively promote private capital’s risk taking; voice has an opposite effect. (4) Multilateral financial institutions’ participation in Green PPP projects promotes higher private capital risk taking.


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## Body

## 1. Introduction

Green infrastructure construction can break through the limitations of traditional ecological protection and help realize the coordinated and sustainable development of ecology, society, and economy. Traditionally, most green infrastructure construction capital comes from the public, especially the governments. However, after the global financial crisis, developed economies’ governments face the pressure of fiscal deficit and deleverage, and developing economies’ financial systems cannot meet the capital demands of green infrastructure construction. Moreover, existing international financial organizations provide insufficient support to fill the gap [1]. Determining ways to introduce new investment subjects and innovating financing channels in green infrastructure construction is an urgent problem to solve the capital shortage. In recent years, some economies have been exploring ways to introduce private capital into green infrastructure construction, of which the green public-private partnership (Green PPP) model is considered the best. Green PPP means that government capital and social capital provide green technology facilities and services to the public in a cooperative manner to meet social development and public demand. Currently, Green PPP is considered the most effective way to address capital shortage for green infrastructure construction. In developed countries, there is a significant growth in private capital participation in green infrastructure construction, and Green PPP infrastructure construction projects are emerging in developing countries [2, 3].African countries are developing rapidly and face great demand for green infrastructure construction. IMF’s African department noted that PPP is an important model to solve the problem of Africa’s great infrastructure construction demand. Companies in various countries are actively “going global,” and private capital is increasingly willing to participate in African Green PPP infrastructure construction projects. However, there are huge differences among African countries, and there are many investment risks. Private capital investment in Green PPP projects in Africa is still in the exploratory stage. Thus, it is necessary to study existing Green PPP projects in African countries and explore their success factors. This is of great significance to global private capital’s participation and success in African Green PPP projects. Although multilateral financial institutions such as the Asian Infrastructure Investment Bank (AIIB) were established to promote infrastructure construction in emerging market countries, some countries have reservations. Despite several studies on the AIIB’s necessity, there is lack of studies demonstrating the role of multilateral financial institutions in promoting green infrastructure construction using empirical research methods.Our study focuses on three issues: (1) Why do Green PPP projects have different results (success or failure) in different countries? (2) Which factors affect Green PPP projects’ success or failure? (3) Which factors affect private capital’s participation degree in Green PPP projects? Answering these questions is essential for global private capital willing to participate in African countries’ Green PPP projects. Our study examines 552 Green PPP projects in African countries for 2005-2019 using the Probit and Cross-section regression models to answer these questions. The main conclusions are as follows: (1) Green PPP project experience affects new Green PPP projects’ results. Countries with successful Green PPP project experience increase in the new Green PPP projects’ success rate, while countries with failed Green PPP project have experience the opposite effect. (2) The more risks private capital takes in Green PPP projects, the higher is the likelihood of project failure. (3) Institution quality can influence the success of a Green PPP project by affecting private capital’s choice of risk-taking degree in the project, and this latter is a mixed effect:rule of law, government effectiveness, control of corruption, and regulatory quality promote private capital’s risk taking in Green PPP projects; voice has an opposite effect. (4) Multilateral financial institutions’ involvement in Green PPP projects’ financing channels promote private capital’s higher risk taking.This study contributes academically by conducting further empirical research on Green PPP issues. It identifies how institution quality and multilateral financial institutions affect Green PPP projects’ internal risk structure and results (success or failure). Although prior literature studied similar problems, there were shortcomings. For example, qualitative research was often adopted to discuss institution quality’s impact on PPP project success, but the channel affecting this success is a black box. This study’s marginal contribution lies in quantifying institution quality through empirical research and internalizing it into the private capital’s choice of a Green PPP project’s internal risk structure. The logic is that institution quality affects private capital’s risk-taking degree in a Green PPP project, which is reflected by the project’s internal risk structure—an important factor determining Green PPP projects’ success or failure. Green PPP projects’ internal risk structure reflects the degree of risk transfer from public to private capital and is based on private capital’s choice of risk-taking degree in the project. As a policy implication, this study recommends the problems global private capital should focus on when participating in African green infrastructure construction, and it demonstrates the importance of multilateral financial institutions, such as AIIB, in promoting and guaranteeing global private capital participation in African green infrastructure construction.The rest of the study is organized as follows. Section2 reviews the literature and develops research hypotheses. Section 3 presents data sources and empirical research methods. Section 4 discusses the regression results. Section 5 concludes with some suggestions.


## 2. Literature Review and Research Hypotheses

### 2.1. Green PPP and Its Internal Risk Structure Classification

The internal risk structure is the core of Green PPP research; different internal risk structures of Green PPP projects reflect different degrees of private capital participation. In the broad sense, Green PPP refers to all types of cooperative relationships the government establishes with private capital in the provision of green technology facilities and services. In the narrow sense, Green PPP is a contractual arrangement involving cooperation between government and private capital with risk and profit sharing [4–7]. Thus, Green PPP projects’ process operation involves the local government and private capital teaming up to set up a project company called the special purpose vehicle, and they cooperate in the whole project implementation process by forming a contractual community to optimize risk and profit sharing. Lopes and Caetano [5] summarized Green PPP projects’ characteristics: task bundling, risk transfer, and long-term contracting. Given Green PPP projects’ long-term traits, Martimort and Straub [8] proposed that their contract arrangement should feature moral hazard, limited liability, and nonreversible contract constraints. In Green PPP projects, different contract arrangements between government and private capital represent private capital’s degree of risk taking in the project, thus indicating the different internal risk structures of the Green PPP projects. Private capital can choose different contract arrangements according to the Green PPP project location’s institution quality, and so Green PPP projects have different internal risk structures.According to the World Bank’s Private Participation in Infrastructure (PPI) Database, PPP projects’ contract arrangement as well as internal risk structure can each be divided into 4 types and 12 subtypes. The 4 types are management contract and lease, brownfield, greenfield, and divestiture. Private capital takes on increased risks in each of these PPP project types. The 12 subtypes include management contract; lease contract; rehabilitate, operate, and transfer (ROT); rehabilitate, lease, or rent and transfer (RLT); build, rehabilitate, and transfer (BROT); built, lease, and transfer (BLT); build, operate, and transfer (BOT); build, own, and operate (BOO); merchant; rental; full; and partial.


### 2.2. Factors Influencing the Result (Success or Failure) of a Green PPP Project

A Green PPP project’s result (success or failure) reflects its risks. Some of its risks are endogenous and vary from project to project. Private capital can control them to some extent [9, 10]. Some risks of Green PPP projects are exogenous, which are related to the project location’s economic environment and political system [11–14]. Existing literature found that the following factors generally affect Green PPP projects’ results.


#### 2.2.1. Successful Green PPP Operation Experience in Green PPP Project Location

Successful Green PPP operation experience is usually an open signal of a good cooperation between the government and private capital, which is crucial to new Green PPP projects’ success [15–17]. Some studies suggested that countries with successful Green PPP experience have a higher probability of success when launching new Green PPP projects, whereas countries with failed Green PPP project experience may face the same obstacles and greater risk of failure in new Green PPP projects [18].


#### 2.2.2. Internal Characteristics of a Green PPP Project

The internal risk structure, total amount of project investment, and cycle of the project may all affect Green PPP projects’ results [4, 5, 18]. Specifically, the probability of failure of green PPP projects increases as the degree of risk sharing in the private sector increases, the total project investment increases, and the project cycle lengthens.


#### 2.2.3. Institution Quality of the Green PPP Project Location

A country’s institution quality—comprising the rule of law, government efficiency, corruption, control of private capital, etc., as well as regional and historical factors such as religion and culture—is not easy to quantify. Some studies used the World Bank’s WGI and International Country Risk Guide’s (ICRG) Country Risk Index—the most influential international databases for measuring institution quality—to empirically study the relationship between the PPP location’s institution quality and PPP projects’ success rate [13, 19, 20]. However, none analyzed the relationship’s mechanism. Further research suggests that a Green PPP project’s internal risk structure is related to its location’s institution quality. Its internal risk structure reflects private capital’s participation degree in the project, which is positively correlated with the project location’s institution quality [21, 22].


#### 2.2.4. Financial Channels for a Green PPP Project

Most infrastructure construction projects need external financing. Estache [23] argued that commercial banks always play a self-serving role in PPP projects, and so PPP projects’ success rate cannot improve if commercial banks provide the financing. However, Galilea and Medda [18] empirically discovered that Green PPP projects are more likely to succeed if financed by multilateral financial institutions such as the World Bank or Asian Development Bank. These tend to be nonprofit, and their participation in Green PPP projects anchors the government and private capital into a better contract arrangement and execution.


#### 2.2.5. Macroeconomic Environment of the Green PPP Project Location

A country’s macroeconomic conditions and economic openness are closely related to the infrastructure construction’s efficiency. Private capital tends to invest in countries with stable macroeconomic environments. Development of a financial market and construction of credit culture in the PPP project location are also essential [16].


### 2.3. Research Hypotheses

Many Green PPP projects fail due to lack of private capital’s evaluation of such projects’ long-term uncertainty, making their actual risks significantly higher than expected, resulting in default or renegotiation [24]. When making Green PPP investment decisions, private capital prioritizes governments with experience. If a government has successful (failed/no) Green PPP project experience, a new Green PPP project with this government is more likely to succeed (face risks). Therefore, we conclude Hypothesis 1.Hypothesis 1.
For African countries, new Green PPP projects’ success rate is positively correlated with experience of successful Green PPP projects.A Green PPP project’s internal risk structure reflects the degree of risk transfer from the government to private capital, and private capital chooses its risk-sharing degree according to project information and own characteristics. To achieve higher profit, private capital must find a balance between taking more risks and making more profits: it may have a subjective desire to increase risk taking, thus increasing Green PPP projects’ failure rate.Hypothesis 2.
Private capital’s choice of risk-sharing degree is negatively correlated with a Green PPP project’s success rate.A good political and judicial system reduces the transaction cost and political risk of private capital’s participation in Green PPP projects. When choosing the degree of risk sharing in a Green PPP project, private capital judges whether the local government has an adequately credible commitment to ensure Green PPP projects’ progress, there are clear measures and schedules to implement the Green PPP contract, there are reasonable incentive pricing mechanisms, and so on. According to the WGI database, institution quality includes six specific indicators—control of corruption, voice and accountability, political stability/no violence, rule of law, government effectiveness, and regulatory quality—and the higher is their value, the better can the country’s institution quality be considered and quantified. Some literature empirically tested these indicators’ positive effects on private capital’s risk-sharing degree in PPP projects [18–20]. However, our study argues that, among these indicators, “voice and accountability” has certain particularity; as its judgment criteria is subjective, the databases judge “voice and accountability” based on developed countries’ principle of the political system. The abovementioned empirical literature was also based on developed countries’ PPP projects, while our study focuses on Green PPP projects in African countries; therefore, the judgment criteria for “voice and accountability” is different. More importantly, there has been controversy about the impact of democratic decentralization and government centralization on the supply of public goods. The concentration of government resources and power can, to a certain extent, increase the government’s mobilization and action power, reduce uncertain risks and transaction costs of Green PPP projects, and create greater incentives for private capital, so as to promote private capital’s participation in Green PPP projects to a greater extent.Institution quality influences Green PPP projects’ internal risk structure by influencing private capital’s degree of participation, thus influencing the result (success or failure) of Green PPP projects. When selecting Green PPP projects’ risk structure, rational private capital selects the most suitable risk-sharing degree according to the institution quality of the Green PPP project location; that is, the institution quality is internalized in a Green PPP project’s internal risk structure. Accordingly, we hypothesize the following:Hypothesis 3.
Institution quality has a mixed effect on a Green PPP project’s success. The five institution quality indicators of control of corruption, political stability/no violence, rule of law, government effectiveness, and regulatory quality positively affect private capital’s risk-sharing degree in a Green PPP project, while the indicator of voice and accountability has the opposite effect.Generally, private capital is at a disadvantage when cooperating with the government. It is unable to strictly supervise the government, making the government prone to abuse its power and increase Green PPP projects’ risks. Multilateral financial institutions’ participation in Green PPP projects is conductive to forming a multilateral cooperation mechanism, implementing international standards, and effectively supervising the government. For example, commercial financial institutions can provide financing for a Green PPP project, but cannot effectively supervise the government, but multilateral financial institutions are nongovernmental organizations, which usually pool sovereign wealth from different countries and negotiate with governments on behalf of donor countries to reach a Green PPP project contract arrangement. Benefitting from their underlying sovereign power, multilateral financial institutions can urge governments to ensure Green PPP projects’ smooth operation to reduce private capital’s risk in participating in the Green PPP project. Therefore, we propose the following.Hypothesis 4.
Multilateral financial institutions’ participation increases private capital’s risk-sharing degree in a Green PPP project.


## 2.1. Green PPP and Its Internal Risk Structure Classification

The internal risk structure is the core of Green PPP research; different internal risk structures of Green PPP projects reflect different degrees of private capital participation. In the broad sense, Green PPP refers to all types of cooperative relationships the government establishes with private capital in the provision of green technology facilities and services. In the narrow sense, Green PPP is a contractual arrangement involving cooperation between government and private capital with risk and profit sharing [4–7]. Thus, Green PPP projects’ process operation involves the local government and private capital teaming up to set up a project company called the special purpose vehicle, and they cooperate in the whole project implementation process by forming a contractual community to optimize risk and profit sharing. Lopes and Caetano [5] summarized Green PPP projects’ characteristics: task bundling, risk transfer, and long-term contracting. Given Green PPP projects’ long-term traits, Martimort and Straub [8] proposed that their contract arrangement should feature moral hazard, limited liability, and nonreversible contract constraints. In Green PPP projects, different contract arrangements between government and private capital represent private capital’s degree of risk taking in the project, thus indicating the different internal risk structures of the Green PPP projects. Private capital can choose different contract arrangements according to the Green PPP project location’s institution quality, and so Green PPP projects have different internal risk structures.According to the World Bank’s Private Participation in Infrastructure (PPI) Database, PPP projects’ contract arrangement as well as internal risk structure can each be divided into 4 types and 12 subtypes. The 4 types are management contract and lease, brownfield, greenfield, and divestiture. Private capital takes on increased risks in each of these PPP project types. The 12 subtypes include management contract; lease contract; rehabilitate, operate, and transfer (ROT); rehabilitate, lease, or rent and transfer (RLT); build, rehabilitate, and transfer (BROT); built, lease, and transfer (BLT); build, operate, and transfer (BOT); build, own, and operate (BOO); merchant; rental; full; and partial.


## 2.2. Factors Influencing the Result (Success or Failure) of a Green PPP Project

A Green PPP project’s result (success or failure) reflects its risks. Some of its risks are endogenous and vary from project to project. Private capital can control them to some extent [9, 10]. Some risks of Green PPP projects are exogenous, which are related to the project location’s economic environment and political system [11–14]. Existing literature found that the following factors generally affect Green PPP projects’ results.


### 2.2.1. Successful Green PPP Operation Experience in Green PPP Project Location

Successful Green PPP operation experience is usually an open signal of a good cooperation between the government and private capital, which is crucial to new Green PPP projects’ success [15–17]. Some studies suggested that countries with successful Green PPP experience have a higher probability of success when launching new Green PPP projects, whereas countries with failed Green PPP project experience may face the same obstacles and greater risk of failure in new Green PPP projects [18].


### 2.2.2. Internal Characteristics of a Green PPP Project

The internal risk structure, total amount of project investment, and cycle of the project may all affect Green PPP projects’ results [4, 5, 18]. Specifically, the probability of failure of green PPP projects increases as the degree of risk sharing in the private sector increases, the total project investment increases, and the project cycle lengthens.


### 2.2.3. Institution Quality of the Green PPP Project Location

A country’s institution quality—comprising the rule of law, government efficiency, corruption, control of private capital, etc., as well as regional and historical factors such as religion and culture—is not easy to quantify. Some studies used the World Bank’s WGI and International Country Risk Guide’s (ICRG) Country Risk Index—the most influential international databases for measuring institution quality—to empirically study the relationship between the PPP location’s institution quality and PPP projects’ success rate [13, 19, 20]. However, none analyzed the relationship’s mechanism. Further research suggests that a Green PPP project’s internal risk structure is related to its location’s institution quality. Its internal risk structure reflects private capital’s participation degree in the project, which is positively correlated with the project location’s institution quality [21, 22].


### 2.2.4. Financial Channels for a Green PPP Project

Most infrastructure construction projects need external financing. Estache [23] argued that commercial banks always play a self-serving role in PPP projects, and so PPP projects’ success rate cannot improve if commercial banks provide the financing. However, Galilea and Medda [18] empirically discovered that Green PPP projects are more likely to succeed if financed by multilateral financial institutions such as the World Bank or Asian Development Bank. These tend to be nonprofit, and their participation in Green PPP projects anchors the government and private capital into a better contract arrangement and execution.


### 2.2.5. Macroeconomic Environment of the Green PPP Project Location

A country’s macroeconomic conditions and economic openness are closely related to the infrastructure construction’s efficiency. Private capital tends to invest in countries with stable macroeconomic environments. Development of a financial market and construction of credit culture in the PPP project location are also essential [16].


## 2.2.1. Successful Green PPP Operation Experience in Green PPP Project Location

Successful Green PPP operation experience is usually an open signal of a good cooperation between the government and private capital, which is crucial to new Green PPP projects’ success [15–17]. Some studies suggested that countries with successful Green PPP experience have a higher probability of success when launching new Green PPP projects, whereas countries with failed Green PPP project experience may face the same obstacles and greater risk of failure in new Green PPP projects [18].


## 2.2.2. Internal Characteristics of a Green PPP Project

The internal risk structure, total amount of project investment, and cycle of the project may all affect Green PPP projects’ results [4, 5, 18]. Specifically, the probability of failure of green PPP projects increases as the degree of risk sharing in the private sector increases, the total project investment increases, and the project cycle lengthens.


## 2.2.3. Institution Quality of the Green PPP Project Location

A country’s institution quality—comprising the rule of law, government efficiency, corruption, control of private capital, etc., as well as regional and historical factors such as religion and culture—is not easy to quantify. Some studies used the World Bank’s WGI and International Country Risk Guide’s (ICRG) Country Risk Index—the most influential international databases for measuring institution quality—to empirically study the relationship between the PPP location’s institution quality and PPP projects’ success rate [13, 19, 20]. However, none analyzed the relationship’s mechanism. Further research suggests that a Green PPP project’s internal risk structure is related to its location’s institution quality. Its internal risk structure reflects private capital’s participation degree in the project, which is positively correlated with the project location’s institution quality [21, 22].


## 2.2.4. Financial Channels for a Green PPP Project

Most infrastructure construction projects need external financing. Estache [23] argued that commercial banks always play a self-serving role in PPP projects, and so PPP projects’ success rate cannot improve if commercial banks provide the financing. However, Galilea and Medda [18] empirically discovered that Green PPP projects are more likely to succeed if financed by multilateral financial institutions such as the World Bank or Asian Development Bank. These tend to be nonprofit, and their participation in Green PPP projects anchors the government and private capital into a better contract arrangement and execution.


## 2.2.5. Macroeconomic Environment of the Green PPP Project Location

A country’s macroeconomic conditions and economic openness are closely related to the infrastructure construction’s efficiency. Private capital tends to invest in countries with stable macroeconomic environments. Development of a financial market and construction of credit culture in the PPP project location are also essential [16].


## 2.3. Research Hypotheses

Many Green PPP projects fail due to lack of private capital’s evaluation of such projects’ long-term uncertainty, making their actual risks significantly higher than expected, resulting in default or renegotiation [24]. When making Green PPP investment decisions, private capital prioritizes governments with experience. If a government has successful (failed/no) Green PPP project experience, a new Green PPP project with this government is more likely to succeed (face risks). Therefore, we conclude Hypothesis 1.Hypothesis 1.
For African countries, new Green PPP projects’ success rate is positively correlated with experience of successful Green PPP projects.A Green PPP project’s internal risk structure reflects the degree of risk transfer from the government to private capital, and private capital chooses its risk-sharing degree according to project information and own characteristics. To achieve higher profit, private capital must find a balance between taking more risks and making more profits: it may have a subjective desire to increase risk taking, thus increasing Green PPP projects’ failure rate.Hypothesis 2.
Private capital’s choice of risk-sharing degree is negatively correlated with a Green PPP project’s success rate.A good political and judicial system reduces the transaction cost and political risk of private capital’s participation in Green PPP projects. When choosing the degree of risk sharing in a Green PPP project, private capital judges whether the local government has an adequately credible commitment to ensure Green PPP projects’ progress, there are clear measures and schedules to implement the Green PPP contract, there are reasonable incentive pricing mechanisms, and so on. According to the WGI database, institution quality includes six specific indicators—control of corruption, voice and accountability, political stability/no violence, rule of law, government effectiveness, and regulatory quality—and the higher is their value, the better can the country’s institution quality be considered and quantified. Some literature empirically tested these indicators’ positive effects on private capital’s risk-sharing degree in PPP projects [18–20]. However, our study argues that, among these indicators, “voice and accountability” has certain particularity; as its judgment criteria is subjective, the databases judge “voice and accountability” based on developed countries’ principle of the political system. The abovementioned empirical literature was also based on developed countries’ PPP projects, while our study focuses on Green PPP projects in African countries; therefore, the judgment criteria for “voice and accountability” is different. More importantly, there has been controversy about the impact of democratic decentralization and government centralization on the supply of public goods. The concentration of government resources and power can, to a certain extent, increase the government’s mobilization and action power, reduce uncertain risks and transaction costs of Green PPP projects, and create greater incentives for private capital, so as to promote private capital’s participation in Green PPP projects to a greater extent.Institution quality influences Green PPP projects’ internal risk structure by influencing private capital’s degree of participation, thus influencing the result (success or failure) of Green PPP projects. When selecting Green PPP projects’ risk structure, rational private capital selects the most suitable risk-sharing degree according to the institution quality of the Green PPP project location; that is, the institution quality is internalized in a Green PPP project’s internal risk structure. Accordingly, we hypothesize the following:Hypothesis 3.
Institution quality has a mixed effect on a Green PPP project’s success. The five institution quality indicators of control of corruption, political stability/no violence, rule of law, government effectiveness, and regulatory quality positively affect private capital’s risk-sharing degree in a Green PPP project, while the indicator of voice and accountability has the opposite effect.Generally, private capital is at a disadvantage when cooperating with the government. It is unable to strictly supervise the government, making the government prone to abuse its power and increase Green PPP projects’ risks. Multilateral financial institutions’ participation in Green PPP projects is conductive to forming a multilateral cooperation mechanism, implementing international standards, and effectively supervising the government. For example, commercial financial institutions can provide financing for a Green PPP project, but cannot effectively supervise the government, but multilateral financial institutions are nongovernmental organizations, which usually pool sovereign wealth from different countries and negotiate with governments on behalf of donor countries to reach a Green PPP project contract arrangement. Benefitting from their underlying sovereign power, multilateral financial institutions can urge governments to ensure Green PPP projects’ smooth operation to reduce private capital’s risk in participating in the Green PPP project. Therefore, we propose the following.Hypothesis 4.
Multilateral financial institutions’ participation increases private capital’s risk-sharing degree in a Green PPP project.


## 3. Data and Research Methods

### 3.1. Green PPP Project Data

The Green PPP project data used in our study were taken from the World Bank’s PPI database, an authoritative database of private capital’s infrastructure construction involvement in various countries. It covers the major sectors of energy, transportation, communication, and sewage. Our study focuses on Green PPP projects in African countries, and so we selected all 2005-2019 Green PPP project data in sewage and clean energy related sectors for 53 African countries from the PPI database as our empirical research object (Table1).Table 1
Countries and income groups of sample Green PPP projects.
CountryIncome levelCountryIncome levelCountryIncome levelAlgeriaUMGabonUMRwandaLowAngolaUMGambiaLowSenegalLMBeninLowGhanaLMSeychellesUMBotswanaUMGuineaLowSie. LeoneLowBurkina FasoLowGuinea-BissauLowSomaliaLowBurundiLowKenyaLowS. AfricaUMCamerounLMLesothoLMS. SudanLMCape VerdeLMLiberiaLowSudanLMC. AfricanLowMadagascarLowSwazilandLMChadLowMalawiLowSTPLMComorosLowMaliLowTanzaniaLowDem. Rep. CongoLowMauritaniaLowTogoLowRep. CongoLMMauritiusUMTunisiaUMIvory CoastLMMoroccoLMUgandaLowDjiboutiLMMozambiqueLowZambiaLMEgyptLMNamibiaUMZimbabweLowEritreaLowNigerLow——EthiopiaLowNigeriaLM——Source: Private Participation in Infrastructure Dataset, World Bank. Notes: Green PPP = green public–private partnership; LM = lower-middle-income country; UM = upper-middle-income country.The Green PPI database mainly provides the following information about Green PPP projects.


#### 3.1.1. Financial Closure Year

This is the year of a Green PPP project’s beginning. This is when the government and private capital formed a legally binding contract arrangement in which both share project risks. It is also the completion date of the Green PPP project-financing plan.


#### 3.1.2. Project Status

This refers to the Green PPP project’s current status. The PPI database defined the “project status” based on whether the project operates normally within the Green PPP contract term, and it assigned six status types to all Green PPP projects: operational, construction, concluded, cancelled, distressed, and merged. Among them, a “merged” Green PPP project is one that has been renamed or replaced; that is, its observed data are invalid. Therefore, Green PPP projects with “merged” status are excluded from our empirical analysis. Most Green PPP projects’ status is “operational” or “construction,” meaning they are not really finished; they will continue to be affected by different factors in the following period and may eventually succeed or fail miserably. Given the high uncertainty of ongoing Green PPP projects, our definition of their “success” or “failure” should be limited to completed projects. Specifically, the definition of a Green PPP project’s “success” or “failure” in our study is as follows: if a Green PPP project’s project status is “concluded,” that project is a “success,” and the value of dependent variable status is 1 in the empirical analysis; if the project status is “distressed” or “cancelled,” the Green PPP project is a “failure,” and status equals 0. The summary of project statuses of the Green PPP projects in our study is presented in Table2.Table 2
The summary of the project status of the sampled Green PPP projects.
OngoingCompletedCancelledCrisisTotalLow income17318138212LM income1621378190UM income138921150Total473402217552Source: Private Participation in Infrastructure Dataset, World Bank. Notes: Green PPP = green public–private partnership; LM = lower-middle-income country; UM = upper-middle-income country.


#### 3.1.3. Type/Subtype of PPI

This refers to contract arrangement the type/subtype of each Green PPP project. Different contract arrangements represent different Green PPP projects’ internal risk structures and different degrees of risk taking by private capital. In increasing order of private capital’s risk-taking degree, the PPI database divides the PPIs into four types, that is, management contract and lease, concession, greenfield projects, and divestiture. These Green PPP project types were divided into 12 subtypes, also based on private capital’s risk-taking degree. According to the type/subtype of PPI, we constructed the dependent regression variables (Type_level: 1-4 and SubType_level: 1-12). Specific construction results are as follows: management contract and lease,Type_level=1; concession, Type_level=2; greenfield projects, Type_level=3; and divestiture, Type_level=4. The 12 subtypes were assigned the following values: management contract, SubType_level=1; full, SubType_level=2; the values in the middle increase in order. Summary of Green PPP projects’ type/subtype of PPI in our empirical test sample is presented in Tables 3 and 4.Table 3
Type/subtype of sampled Green PPP projects and their variable value.
Type_levelContractsBrownfieldVariable assignment12Subtype_levelManagement contractLease contractROTRLTBROTVariable assignment12345Type_levelGreenfieldDivestitureVariable assignment34Subtype_levelBLTBOTBOOMerchantRentalPartialFullVariable assignment6789101112Source: Private Participation in Infrastructure Dataset, World Bank. Notes: Green PPP = green public–private partnership; ROT = rehabilitate, operate, and transfer; RLT = rehabilitate, lease, or rent and transfer; BROT = build, rehabilitate, and transfer; BLT = built, lease, and transfer; BOT = build, operate, and transfer; BOO = build, own, and operate.Table 4
Summary of the type/subtype of PPI of the sampled Green PPP projects.
Type_levelSubtype_levelContractsLow incomeLM incomeUM incomeTotal11Management contract101210322Lease contract360923ROT11134284RLT5200255BROT41362336BLT00117BOT183216668BOO3632791479Merchant92472116010Rental152724411Partial141463412Full1203Total209193150552Source: Private Participation in Infrastructure Dataset, World Bank. Notes: PPI = private participation in infrastructure; Green PPP = green public–private partnership; LM = lower-middle-income country; UM = upper-middle-income country; ROT = rehabilitate, operate, and transfer; RLT = rehabilitate, lease, or rent and transfer; BROT = build, rehabilitate, and transfer; BLT = built, lease, and transfer; BOT = build, operate, and transfer; BOO = build, own, and operate.


#### 3.1.4. Sponsors

Sponsors are private capital investors who are the real risk takers in the Green PPP project. In our study, we constructed dependent regression variables (DummySponsors=0 or 1) to measure whether Green PPP projects involve one or more private capital investors. DummySponsors equals 0 if a PPP project has only one private capital investor and 1 for more than one investor.


#### 3.1.5. Multilateral Support

This refers to whether a Green PPP project involves multilateral financial institutions, and we expressed this using the Multilender (=0 or 1)-dependent regression variable. Specifically, Multilender equals 1 if a Green PPP project involves multilateral financial institutions and 0 otherwise.


#### 3.1.6. Investment and Period

The PPI database provides information on Green PPP projects’ investment amount and project period, which are two important indicators to control different Green PPP projects’ risks. A higher investment amount or longer project period means the Green PPP project will face more uncertainties. Therefore, we constructed two control variables: investment and period.


### 3.2. Other Relevant Data

#### 3.2.1. Green PPP Project Experience

According to the PPI database, our study used the number of successful Green PPP projects in a location country before launching a new Green PPP project as the measurement index of successful project experience (SuccessPPI). Similarly, we used the number of failed projects as a measure of experience with failed projects (NoSuccessPPI).


#### 3.2.2. Institution Quality

The quantification of institution quality is often subjective and difficult. Our study used the World Bank WGI database, which includes six specific indicators to measure institution quality (Table5). Among these, (a) control of corruption (Anti_Corruption) reflects a government’s ability to monitor, prevent, and control corruption. The variable Anti_Corruption ranges from -2.5 to 2.5, with a higher value indicating the government’s ability to control corruption. (b) Voice and accountability (Voice) reflects a country’s degree of democracy. It mainly includes two aspects: whether people have the right to elect the government and whether the public media are not restricted in the process of news reporting. Voice ranges from -2.5 to 2.5, with a higher value indicating the people’s right to speak and the public’s influence on social and economic affairs. (c) Political stability/no violence (Stability) reflects a government’s possibility of being disturbed/overturned by a violation of the constitution or violence. Stability ranges from -2.5 to 2.5, with a higher value indicating the degree of government’s stability. (d) Rule of law (Law) reflects the degree of compliance with the laws of social subjects, quality of contract execution, property rights protection, police and courts, and degree of violence and crime. Law ranges from -2.5 to 2.5, with a higher value indicating the degree of the rule of law. (e) Government effectiveness (Effectiveness) reflects the quality of public service provided by the government, including the quality of policy setting and implementation and credibility of government commitments. Effectiveness ranges from -2.5 to 2.5, with a higher value indicating the government’s effectiveness. (f) Regulatory quality (Regulation) reflects the government’s ability to set and implement policies that benefit private capital development. Regulation ranges from -2.5 to 2.5, with a higher value indicating the government’s positive role in promoting private capital development.Table 5
The summary of the Institution Quality.
VariableIndexRangeDefinitionAnti_CorruptionControl of corruption[-2.5,2.5]The ability of a government to prevent and control corruptionVoiceVoice and accountability[-2.5,2.5]The level of democratization in a country, including whether civilians have the right to voteStabilityPolitical stability no violence[-2.5,2.5]The regime’s stabilityLawRule of law[-2.5,2.5]Institutionalized level in one countryEffectivenessGovernment effectiveness[-2.5,2.5]Quality of public service and credibility of governmentRegulationRegulatory quality[-2.5,2.5]Whether the government is willing to implement policies to improve the private sectorSource: Worldwide Governance Indicators database, World Bank.The World Bank WGI database gives specific values to these six variables in countries where the Green PPP projects in our study are located. Considering the multicollinearity problem, we calculated these six variables’ correlation coefficients and found that Anti_Corruption had high positive correction with Law, Effectiveness, and Regulation. Therefore, only Anti_Corruption, Voice, and Stability were retained in the following regression analysis.


#### 3.2.3. Control Variables

The control variables include the following.(1) National Income Level (Income). The level of a country’s income usually affects investors’ enthusiasm for investment. We divided the sample countries into low income (Low_Income), lower middle income (Lower_Middle_Income), and upper middle income (Upper_Middle_Income) according to the PPI classification criteria for national income levels and assigned them values 1, 2, and 3, respectively, in the first empirical regression analysis. In the second empirical regression analysis, taking low-income as the benchmark, we introduced dummy variables i_Lower_Middle_Income and i_Upper_Middle_Income.(2) GDP Growth Rate (Ln_L_GDP_Growth). GDP growth rate in the year of a Green PPP project’s start can affect the demand for green infrastructure construction and enthusiasm of private capital investment. We construct variable Ln_L_GDP_Growth and use GDP growth rate data for a Green PPP project’s financial closure year.(3) Capital Opening Index (L_Open). The degree of capital openness in the Green PPP project locations can affect a Green PPP project’s results (success or failure) and private capital’s degree of participation. Therefore, we introduce capital opening related variables into the empirical regression. We constructed the capital opening index, calculated as Open_indext=FDIt/GDPt. We construct control variable L_Open, whose value is one lag phase value of the capital opening index.(4) Temporal Trend (i_Year). To eliminate the temporal trend’s influence, we introduce the time control variable i_Year into the empirical regression, which takes the specific value of 1 when the year is 2005 and 15 when the year is 2019; the rest may be deduced by analogy.


### 3.3. Research Methods

#### 3.3.1. Regression Model 1: Factors Influencing Green PPP Projects’ Success or Failure

The first regression model empirically analyzes factors influencing Green PPP projects’ success or failure of a (Status). The dependent variable is Green PPP projects’ success or failure, which is a binary variable. Probit model is mostly used for binary variables. Its advantage lies in its simple form and easy processing. It is the most basic and effective statistical model. In many cases, there is no linear relationship between independent variables and dependent variables, so the model can achieve the original linear hypothesis by adjusting the fitting appropriately. Following Percoco [21], we adopt the Probit model in the first regression:
(1)PY=1X=eX′β1+eX′β,where X is the independent variable and β is the regression coefficient. In this regression model, Green PPP project experience (SuccessPPI or NoSuccessPPI), type/subtype of PPI (Type_level and SubType_level), sponsors (DummySponsors), investment (Investment), institution quality (Corruption, Voice, and Stability), and other control variables (e.g., Income and L_Open) are independent variables. The dependent variable is project status (Status). If the project status of a Green PPP project is “concluded,” Status takes the value of 1 and 0 if the project status is “distressed” or “cancelled.” Table 6 describes the variables statistics in regression model 1.Table 6
Summary statistics of variables in regression model 1.
VariableObservationMeanStd. devMinimumMaximumStatus790.2420.3350.0001.000SuccessPPI791.6121.8810.0007.000NoSuccessPPI792.0122.1150.0005.000DummySponsors790.3120.4370.0001.000Investment79104.666397.0820.0002,990.883Type_level791.9260.8941.0004.000Subtype_level795.3203.9781.00012.000Voice79-0.1090.302-1.9720.402Anti_Corruption79-0.6920.298-1.0840.320Stability79-0.9080.569-2.2630.414L_Open7911.13111.7551.22884.198Income791.6580.7321.0003.000


#### 3.3.2. Regression Model 2: Factors Influencing Green PPP Projects’ Internal Risk Structures

Regression model 1 reveals which factors will contribute to the success of a Green PPP project. Among them, we focus on the influence of internal risk structures. Then, the next question is, which factors affect a Green PPP project’s internal risk structure? Which factors affect private capital’s risk-taking degree in a Green PPP project? To answer these questions, we adopted cross-section regression model 2 to analyze factors influencing a Green PPP project’s internal risk structure. The advantage of cross-section model is that it can effectively solve the problem of insufficient sample size, estimate the impact of some factors that are difficult to measure on the explained variables, and help correctly understand the relationship between economic variables.(2)RiskSharing=α+∑i=1mηi⋅QIt+γ⋅Multilender+∑j=1nσj⋅Propj+∑I=1qθt⋅ControlI+ξ,where RiskSharing is the dependent variable representing private capital’s risk-taking degree in a Green PPP project. Private capital’s different risk-taking degrees in Green PPP projects are given by different contract arrangements of Green PPP projects, represented by variables Type_level and SubType_level. In regression model 2, the independent variables include institution quality (Corruption, Voice, and Stability), multilateral support (Multilender),Propj (DummySponsors, Investment, and Period),ControlI (L_Open, i_Low_Income, i_Lower_Middle_Income, i_Upper_Middle_Income, Ln_L_GDP_Growth, and i_Year). Regression model 2 only uses successful projects as research samples. Table 7 describes the variables’ statistics in regression model 2.Table 7
Summary statistics of variables in regression model 2.
VariableObservationMeanStd. devMinimumMaximumType_level5131.4260.5131.0004.000Subtype_level5135.0881.8181.00012.000Voice513-0.5240.767-1.8750.463Anti_Corruption513-0.6890.254-1.4650.547Stability513-0.9730.407-2.4401.209Multilender5130.0210.1670.0001.000DummySponsors5130.2260.4620.0001.000Investment513649.5591,108.2400.0004,093.656Period51325.47012.4831.00099.000L_Open51315.19813.0130.762109.427I_Year5134.3043.1821.00012.000i_Lower_Middle_Income5130.4020.3830.0001.000i_Upper_Middle_Income5130.4470.3940.0001.000Ln_L_GDP_Growth5132.9841.480-1.5005.500


## 3.1. Green PPP Project Data

The Green PPP project data used in our study were taken from the World Bank’s PPI database, an authoritative database of private capital’s infrastructure construction involvement in various countries. It covers the major sectors of energy, transportation, communication, and sewage. Our study focuses on Green PPP projects in African countries, and so we selected all 2005-2019 Green PPP project data in sewage and clean energy related sectors for 53 African countries from the PPI database as our empirical research object (Table1).Table 1
Countries and income groups of sample Green PPP projects.
CountryIncome levelCountryIncome levelCountryIncome levelAlgeriaUMGabonUMRwandaLowAngolaUMGambiaLowSenegalLMBeninLowGhanaLMSeychellesUMBotswanaUMGuineaLowSie. LeoneLowBurkina FasoLowGuinea-BissauLowSomaliaLowBurundiLowKenyaLowS. AfricaUMCamerounLMLesothoLMS. SudanLMCape VerdeLMLiberiaLowSudanLMC. AfricanLowMadagascarLowSwazilandLMChadLowMalawiLowSTPLMComorosLowMaliLowTanzaniaLowDem. Rep. CongoLowMauritaniaLowTogoLowRep. CongoLMMauritiusUMTunisiaUMIvory CoastLMMoroccoLMUgandaLowDjiboutiLMMozambiqueLowZambiaLMEgyptLMNamibiaUMZimbabweLowEritreaLowNigerLow——EthiopiaLowNigeriaLM——Source: Private Participation in Infrastructure Dataset, World Bank. Notes: Green PPP = green public–private partnership; LM = lower-middle-income country; UM = upper-middle-income country.The Green PPI database mainly provides the following information about Green PPP projects.


### 3.1.1. Financial Closure Year

This is the year of a Green PPP project’s beginning. This is when the government and private capital formed a legally binding contract arrangement in which both share project risks. It is also the completion date of the Green PPP project-financing plan.


### 3.1.2. Project Status

This refers to the Green PPP project’s current status. The PPI database defined the “project status” based on whether the project operates normally within the Green PPP contract term, and it assigned six status types to all Green PPP projects: operational, construction, concluded, cancelled, distressed, and merged. Among them, a “merged” Green PPP project is one that has been renamed or replaced; that is, its observed data are invalid. Therefore, Green PPP projects with “merged” status are excluded from our empirical analysis. Most Green PPP projects’ status is “operational” or “construction,” meaning they are not really finished; they will continue to be affected by different factors in the following period and may eventually succeed or fail miserably. Given the high uncertainty of ongoing Green PPP projects, our definition of their “success” or “failure” should be limited to completed projects. Specifically, the definition of a Green PPP project’s “success” or “failure” in our study is as follows: if a Green PPP project’s project status is “concluded,” that project is a “success,” and the value of dependent variable status is 1 in the empirical analysis; if the project status is “distressed” or “cancelled,” the Green PPP project is a “failure,” and status equals 0. The summary of project statuses of the Green PPP projects in our study is presented in Table2.Table 2
The summary of the project status of the sampled Green PPP projects.
OngoingCompletedCancelledCrisisTotalLow income17318138212LM income1621378190UM income138921150Total473402217552Source: Private Participation in Infrastructure Dataset, World Bank. Notes: Green PPP = green public–private partnership; LM = lower-middle-income country; UM = upper-middle-income country.


### 3.1.3. Type/Subtype of PPI

This refers to contract arrangement the type/subtype of each Green PPP project. Different contract arrangements represent different Green PPP projects’ internal risk structures and different degrees of risk taking by private capital. In increasing order of private capital’s risk-taking degree, the PPI database divides the PPIs into four types, that is, management contract and lease, concession, greenfield projects, and divestiture. These Green PPP project types were divided into 12 subtypes, also based on private capital’s risk-taking degree. According to the type/subtype of PPI, we constructed the dependent regression variables (Type_level: 1-4 and SubType_level: 1-12). Specific construction results are as follows: management contract and lease,Type_level=1; concession, Type_level=2; greenfield projects, Type_level=3; and divestiture, Type_level=4. The 12 subtypes were assigned the following values: management contract, SubType_level=1; full, SubType_level=2; the values in the middle increase in order. Summary of Green PPP projects’ type/subtype of PPI in our empirical test sample is presented in Tables 3 and 4.Table 3
Type/subtype of sampled Green PPP projects and their variable value.
Type_levelContractsBrownfieldVariable assignment12Subtype_levelManagement contractLease contractROTRLTBROTVariable assignment12345Type_levelGreenfieldDivestitureVariable assignment34Subtype_levelBLTBOTBOOMerchantRentalPartialFullVariable assignment6789101112Source: Private Participation in Infrastructure Dataset, World Bank. Notes: Green PPP = green public–private partnership; ROT = rehabilitate, operate, and transfer; RLT = rehabilitate, lease, or rent and transfer; BROT = build, rehabilitate, and transfer; BLT = built, lease, and transfer; BOT = build, operate, and transfer; BOO = build, own, and operate.Table 4
Summary of the type/subtype of PPI of the sampled Green PPP projects.
Type_levelSubtype_levelContractsLow incomeLM incomeUM incomeTotal11Management contract101210322Lease contract360923ROT11134284RLT5200255BROT41362336BLT00117BOT183216668BOO3632791479Merchant92472116010Rental152724411Partial141463412Full1203Total209193150552Source: Private Participation in Infrastructure Dataset, World Bank. Notes: PPI = private participation in infrastructure; Green PPP = green public–private partnership; LM = lower-middle-income country; UM = upper-middle-income country; ROT = rehabilitate, operate, and transfer; RLT = rehabilitate, lease, or rent and transfer; BROT = build, rehabilitate, and transfer; BLT = built, lease, and transfer; BOT = build, operate, and transfer; BOO = build, own, and operate.


### 3.1.4. Sponsors

Sponsors are private capital investors who are the real risk takers in the Green PPP project. In our study, we constructed dependent regression variables (DummySponsors=0 or 1) to measure whether Green PPP projects involve one or more private capital investors. DummySponsors equals 0 if a PPP project has only one private capital investor and 1 for more than one investor.


### 3.1.5. Multilateral Support

This refers to whether a Green PPP project involves multilateral financial institutions, and we expressed this using the Multilender (=0 or 1)-dependent regression variable. Specifically, Multilender equals 1 if a Green PPP project involves multilateral financial institutions and 0 otherwise.


### 3.1.6. Investment and Period

The PPI database provides information on Green PPP projects’ investment amount and project period, which are two important indicators to control different Green PPP projects’ risks. A higher investment amount or longer project period means the Green PPP project will face more uncertainties. Therefore, we constructed two control variables: investment and period.


## 3.1.1. Financial Closure Year

This is the year of a Green PPP project’s beginning. This is when the government and private capital formed a legally binding contract arrangement in which both share project risks. It is also the completion date of the Green PPP project-financing plan.


## 3.1.2. Project Status

This refers to the Green PPP project’s current status. The PPI database defined the “project status” based on whether the project operates normally within the Green PPP contract term, and it assigned six status types to all Green PPP projects: operational, construction, concluded, cancelled, distressed, and merged. Among them, a “merged” Green PPP project is one that has been renamed or replaced; that is, its observed data are invalid. Therefore, Green PPP projects with “merged” status are excluded from our empirical analysis. Most Green PPP projects’ status is “operational” or “construction,” meaning they are not really finished; they will continue to be affected by different factors in the following period and may eventually succeed or fail miserably. Given the high uncertainty of ongoing Green PPP projects, our definition of their “success” or “failure” should be limited to completed projects. Specifically, the definition of a Green PPP project’s “success” or “failure” in our study is as follows: if a Green PPP project’s project status is “concluded,” that project is a “success,” and the value of dependent variable status is 1 in the empirical analysis; if the project status is “distressed” or “cancelled,” the Green PPP project is a “failure,” and status equals 0. The summary of project statuses of the Green PPP projects in our study is presented in Table2.Table 2
The summary of the project status of the sampled Green PPP projects.
OngoingCompletedCancelledCrisisTotalLow income17318138212LM income1621378190UM income138921150Total473402217552Source: Private Participation in Infrastructure Dataset, World Bank. Notes: Green PPP = green public–private partnership; LM = lower-middle-income country; UM = upper-middle-income country.


## 3.1.3. Type/Subtype of PPI

This refers to contract arrangement the type/subtype of each Green PPP project. Different contract arrangements represent different Green PPP projects’ internal risk structures and different degrees of risk taking by private capital. In increasing order of private capital’s risk-taking degree, the PPI database divides the PPIs into four types, that is, management contract and lease, concession, greenfield projects, and divestiture. These Green PPP project types were divided into 12 subtypes, also based on private capital’s risk-taking degree. According to the type/subtype of PPI, we constructed the dependent regression variables (Type_level: 1-4 and SubType_level: 1-12). Specific construction results are as follows: management contract and lease,Type_level=1; concession, Type_level=2; greenfield projects, Type_level=3; and divestiture, Type_level=4. The 12 subtypes were assigned the following values: management contract, SubType_level=1; full, SubType_level=2; the values in the middle increase in order. Summary of Green PPP projects’ type/subtype of PPI in our empirical test sample is presented in Tables 3 and 4.Table 3
Type/subtype of sampled Green PPP projects and their variable value.
Type_levelContractsBrownfieldVariable assignment12Subtype_levelManagement contractLease contractROTRLTBROTVariable assignment12345Type_levelGreenfieldDivestitureVariable assignment34Subtype_levelBLTBOTBOOMerchantRentalPartialFullVariable assignment6789101112Source: Private Participation in Infrastructure Dataset, World Bank. Notes: Green PPP = green public–private partnership; ROT = rehabilitate, operate, and transfer; RLT = rehabilitate, lease, or rent and transfer; BROT = build, rehabilitate, and transfer; BLT = built, lease, and transfer; BOT = build, operate, and transfer; BOO = build, own, and operate.Table 4
Summary of the type/subtype of PPI of the sampled Green PPP projects.
Type_levelSubtype_levelContractsLow incomeLM incomeUM incomeTotal11Management contract101210322Lease contract360923ROT11134284RLT5200255BROT41362336BLT00117BOT183216668BOO3632791479Merchant92472116010Rental152724411Partial141463412Full1203Total209193150552Source: Private Participation in Infrastructure Dataset, World Bank. Notes: PPI = private participation in infrastructure; Green PPP = green public–private partnership; LM = lower-middle-income country; UM = upper-middle-income country; ROT = rehabilitate, operate, and transfer; RLT = rehabilitate, lease, or rent and transfer; BROT = build, rehabilitate, and transfer; BLT = built, lease, and transfer; BOT = build, operate, and transfer; BOO = build, own, and operate.


## 3.1.4. Sponsors

Sponsors are private capital investors who are the real risk takers in the Green PPP project. In our study, we constructed dependent regression variables (DummySponsors=0 or 1) to measure whether Green PPP projects involve one or more private capital investors. DummySponsors equals 0 if a PPP project has only one private capital investor and 1 for more than one investor.


## 3.1.5. Multilateral Support

This refers to whether a Green PPP project involves multilateral financial institutions, and we expressed this using the Multilender (=0 or 1)-dependent regression variable. Specifically, Multilender equals 1 if a Green PPP project involves multilateral financial institutions and 0 otherwise.


## 3.1.6. Investment and Period

The PPI database provides information on Green PPP projects’ investment amount and project period, which are two important indicators to control different Green PPP projects’ risks. A higher investment amount or longer project period means the Green PPP project will face more uncertainties. Therefore, we constructed two control variables: investment and period.


## 3.2. Other Relevant Data

### 3.2.1. Green PPP Project Experience

According to the PPI database, our study used the number of successful Green PPP projects in a location country before launching a new Green PPP project as the measurement index of successful project experience (SuccessPPI). Similarly, we used the number of failed projects as a measure of experience with failed projects (NoSuccessPPI).


### 3.2.2. Institution Quality

The quantification of institution quality is often subjective and difficult. Our study used the World Bank WGI database, which includes six specific indicators to measure institution quality (Table5). Among these, (a) control of corruption (Anti_Corruption) reflects a government’s ability to monitor, prevent, and control corruption. The variable Anti_Corruption ranges from -2.5 to 2.5, with a higher value indicating the government’s ability to control corruption. (b) Voice and accountability (Voice) reflects a country’s degree of democracy. It mainly includes two aspects: whether people have the right to elect the government and whether the public media are not restricted in the process of news reporting. Voice ranges from -2.5 to 2.5, with a higher value indicating the people’s right to speak and the public’s influence on social and economic affairs. (c) Political stability/no violence (Stability) reflects a government’s possibility of being disturbed/overturned by a violation of the constitution or violence. Stability ranges from -2.5 to 2.5, with a higher value indicating the degree of government’s stability. (d) Rule of law (Law) reflects the degree of compliance with the laws of social subjects, quality of contract execution, property rights protection, police and courts, and degree of violence and crime. Law ranges from -2.5 to 2.5, with a higher value indicating the degree of the rule of law. (e) Government effectiveness (Effectiveness) reflects the quality of public service provided by the government, including the quality of policy setting and implementation and credibility of government commitments. Effectiveness ranges from -2.5 to 2.5, with a higher value indicating the government’s effectiveness. (f) Regulatory quality (Regulation) reflects the government’s ability to set and implement policies that benefit private capital development. Regulation ranges from -2.5 to 2.5, with a higher value indicating the government’s positive role in promoting private capital development.Table 5
The summary of the Institution Quality.
VariableIndexRangeDefinitionAnti_CorruptionControl of corruption[-2.5,2.5]The ability of a government to prevent and control corruptionVoiceVoice and accountability[-2.5,2.5]The level of democratization in a country, including whether civilians have the right to voteStabilityPolitical stability no violence[-2.5,2.5]The regime’s stabilityLawRule of law[-2.5,2.5]Institutionalized level in one countryEffectivenessGovernment effectiveness[-2.5,2.5]Quality of public service and credibility of governmentRegulationRegulatory quality[-2.5,2.5]Whether the government is willing to implement policies to improve the private sectorSource: Worldwide Governance Indicators database, World Bank.The World Bank WGI database gives specific values to these six variables in countries where the Green PPP projects in our study are located. Considering the multicollinearity problem, we calculated these six variables’ correlation coefficients and found that Anti_Corruption had high positive correction with Law, Effectiveness, and Regulation. Therefore, only Anti_Corruption, Voice, and Stability were retained in the following regression analysis.


### 3.2.3. Control Variables

The control variables include the following.(1) National Income Level (Income). The level of a country’s income usually affects investors’ enthusiasm for investment. We divided the sample countries into low income (Low_Income), lower middle income (Lower_Middle_Income), and upper middle income (Upper_Middle_Income) according to the PPI classification criteria for national income levels and assigned them values 1, 2, and 3, respectively, in the first empirical regression analysis. In the second empirical regression analysis, taking low-income as the benchmark, we introduced dummy variables i_Lower_Middle_Income and i_Upper_Middle_Income.(2) GDP Growth Rate (Ln_L_GDP_Growth). GDP growth rate in the year of a Green PPP project’s start can affect the demand for green infrastructure construction and enthusiasm of private capital investment. We construct variable Ln_L_GDP_Growth and use GDP growth rate data for a Green PPP project’s financial closure year.(3) Capital Opening Index (L_Open). The degree of capital openness in the Green PPP project locations can affect a Green PPP project’s results (success or failure) and private capital’s degree of participation. Therefore, we introduce capital opening related variables into the empirical regression. We constructed the capital opening index, calculated as Open_indext=FDIt/GDPt. We construct control variable L_Open, whose value is one lag phase value of the capital opening index.(4) Temporal Trend (i_Year). To eliminate the temporal trend’s influence, we introduce the time control variable i_Year into the empirical regression, which takes the specific value of 1 when the year is 2005 and 15 when the year is 2019; the rest may be deduced by analogy.


## 3.2.1. Green PPP Project Experience

According to the PPI database, our study used the number of successful Green PPP projects in a location country before launching a new Green PPP project as the measurement index of successful project experience (SuccessPPI). Similarly, we used the number of failed projects as a measure of experience with failed projects (NoSuccessPPI).


## 3.2.2. Institution Quality

The quantification of institution quality is often subjective and difficult. Our study used the World Bank WGI database, which includes six specific indicators to measure institution quality (Table5). Among these, (a) control of corruption (Anti_Corruption) reflects a government’s ability to monitor, prevent, and control corruption. The variable Anti_Corruption ranges from -2.5 to 2.5, with a higher value indicating the government’s ability to control corruption. (b) Voice and accountability (Voice) reflects a country’s degree of democracy. It mainly includes two aspects: whether people have the right to elect the government and whether the public media are not restricted in the process of news reporting. Voice ranges from -2.5 to 2.5, with a higher value indicating the people’s right to speak and the public’s influence on social and economic affairs. (c) Political stability/no violence (Stability) reflects a government’s possibility of being disturbed/overturned by a violation of the constitution or violence. Stability ranges from -2.5 to 2.5, with a higher value indicating the degree of government’s stability. (d) Rule of law (Law) reflects the degree of compliance with the laws of social subjects, quality of contract execution, property rights protection, police and courts, and degree of violence and crime. Law ranges from -2.5 to 2.5, with a higher value indicating the degree of the rule of law. (e) Government effectiveness (Effectiveness) reflects the quality of public service provided by the government, including the quality of policy setting and implementation and credibility of government commitments. Effectiveness ranges from -2.5 to 2.5, with a higher value indicating the government’s effectiveness. (f) Regulatory quality (Regulation) reflects the government’s ability to set and implement policies that benefit private capital development. Regulation ranges from -2.5 to 2.5, with a higher value indicating the government’s positive role in promoting private capital development.Table 5
The summary of the Institution Quality.
VariableIndexRangeDefinitionAnti_CorruptionControl of corruption[-2.5,2.5]The ability of a government to prevent and control corruptionVoiceVoice and accountability[-2.5,2.5]The level of democratization in a country, including whether civilians have the right to voteStabilityPolitical stability no violence[-2.5,2.5]The regime’s stabilityLawRule of law[-2.5,2.5]Institutionalized level in one countryEffectivenessGovernment effectiveness[-2.5,2.5]Quality of public service and credibility of governmentRegulationRegulatory quality[-2.5,2.5]Whether the government is willing to implement policies to improve the private sectorSource: Worldwide Governance Indicators database, World Bank.The World Bank WGI database gives specific values to these six variables in countries where the Green PPP projects in our study are located. Considering the multicollinearity problem, we calculated these six variables’ correlation coefficients and found that Anti_Corruption had high positive correction with Law, Effectiveness, and Regulation. Therefore, only Anti_Corruption, Voice, and Stability were retained in the following regression analysis.


## 3.2.3. Control Variables

The control variables include the following.(1) National Income Level (Income). The level of a country’s income usually affects investors’ enthusiasm for investment. We divided the sample countries into low income (Low_Income), lower middle income (Lower_Middle_Income), and upper middle income (Upper_Middle_Income) according to the PPI classification criteria for national income levels and assigned them values 1, 2, and 3, respectively, in the first empirical regression analysis. In the second empirical regression analysis, taking low-income as the benchmark, we introduced dummy variables i_Lower_Middle_Income and i_Upper_Middle_Income.(2) GDP Growth Rate (Ln_L_GDP_Growth). GDP growth rate in the year of a Green PPP project’s start can affect the demand for green infrastructure construction and enthusiasm of private capital investment. We construct variable Ln_L_GDP_Growth and use GDP growth rate data for a Green PPP project’s financial closure year.(3) Capital Opening Index (L_Open). The degree of capital openness in the Green PPP project locations can affect a Green PPP project’s results (success or failure) and private capital’s degree of participation. Therefore, we introduce capital opening related variables into the empirical regression. We constructed the capital opening index, calculated as Open_indext=FDIt/GDPt. We construct control variable L_Open, whose value is one lag phase value of the capital opening index.(4) Temporal Trend (i_Year). To eliminate the temporal trend’s influence, we introduce the time control variable i_Year into the empirical regression, which takes the specific value of 1 when the year is 2005 and 15 when the year is 2019; the rest may be deduced by analogy.


## 3.3. Research Methods

### 3.3.1. Regression Model 1: Factors Influencing Green PPP Projects’ Success or Failure

The first regression model empirically analyzes factors influencing Green PPP projects’ success or failure of a (Status). The dependent variable is Green PPP projects’ success or failure, which is a binary variable. Probit model is mostly used for binary variables. Its advantage lies in its simple form and easy processing. It is the most basic and effective statistical model. In many cases, there is no linear relationship between independent variables and dependent variables, so the model can achieve the original linear hypothesis by adjusting the fitting appropriately. Following Percoco [21], we adopt the Probit model in the first regression:
(1)PY=1X=eX′β1+eX′β,where X is the independent variable and β is the regression coefficient. In this regression model, Green PPP project experience (SuccessPPI or NoSuccessPPI), type/subtype of PPI (Type_level and SubType_level), sponsors (DummySponsors), investment (Investment), institution quality (Corruption, Voice, and Stability), and other control variables (e.g., Income and L_Open) are independent variables. The dependent variable is project status (Status). If the project status of a Green PPP project is “concluded,” Status takes the value of 1 and 0 if the project status is “distressed” or “cancelled.” Table 6 describes the variables statistics in regression model 1.Table 6
Summary statistics of variables in regression model 1.
VariableObservationMeanStd. devMinimumMaximumStatus790.2420.3350.0001.000SuccessPPI791.6121.8810.0007.000NoSuccessPPI792.0122.1150.0005.000DummySponsors790.3120.4370.0001.000Investment79104.666397.0820.0002,990.883Type_level791.9260.8941.0004.000Subtype_level795.3203.9781.00012.000Voice79-0.1090.302-1.9720.402Anti_Corruption79-0.6920.298-1.0840.320Stability79-0.9080.569-2.2630.414L_Open7911.13111.7551.22884.198Income791.6580.7321.0003.000


### 3.3.2. Regression Model 2: Factors Influencing Green PPP Projects’ Internal Risk Structures

Regression model 1 reveals which factors will contribute to the success of a Green PPP project. Among them, we focus on the influence of internal risk structures. Then, the next question is, which factors affect a Green PPP project’s internal risk structure? Which factors affect private capital’s risk-taking degree in a Green PPP project? To answer these questions, we adopted cross-section regression model 2 to analyze factors influencing a Green PPP project’s internal risk structure. The advantage of cross-section model is that it can effectively solve the problem of insufficient sample size, estimate the impact of some factors that are difficult to measure on the explained variables, and help correctly understand the relationship between economic variables.(2)RiskSharing=α+∑i=1mηi⋅QIt+γ⋅Multilender+∑j=1nσj⋅Propj+∑I=1qθt⋅ControlI+ξ,where RiskSharing is the dependent variable representing private capital’s risk-taking degree in a Green PPP project. Private capital’s different risk-taking degrees in Green PPP projects are given by different contract arrangements of Green PPP projects, represented by variables Type_level and SubType_level. In regression model 2, the independent variables include institution quality (Corruption, Voice, and Stability), multilateral support (Multilender),Propj (DummySponsors, Investment, and Period),ControlI (L_Open, i_Low_Income, i_Lower_Middle_Income, i_Upper_Middle_Income, Ln_L_GDP_Growth, and i_Year). Regression model 2 only uses successful projects as research samples. Table 7 describes the variables’ statistics in regression model 2.Table 7
Summary statistics of variables in regression model 2.
VariableObservationMeanStd. devMinimumMaximumType_level5131.4260.5131.0004.000Subtype_level5135.0881.8181.00012.000Voice513-0.5240.767-1.8750.463Anti_Corruption513-0.6890.254-1.4650.547Stability513-0.9730.407-2.4401.209Multilender5130.0210.1670.0001.000DummySponsors5130.2260.4620.0001.000Investment513649.5591,108.2400.0004,093.656Period51325.47012.4831.00099.000L_Open51315.19813.0130.762109.427I_Year5134.3043.1821.00012.000i_Lower_Middle_Income5130.4020.3830.0001.000i_Upper_Middle_Income5130.4470.3940.0001.000Ln_L_GDP_Growth5132.9841.480-1.5005.500


## 3.3.1. Regression Model 1: Factors Influencing Green PPP Projects’ Success or Failure

The first regression model empirically analyzes factors influencing Green PPP projects’ success or failure of a (Status). The dependent variable is Green PPP projects’ success or failure, which is a binary variable. Probit model is mostly used for binary variables. Its advantage lies in its simple form and easy processing. It is the most basic and effective statistical model. In many cases, there is no linear relationship between independent variables and dependent variables, so the model can achieve the original linear hypothesis by adjusting the fitting appropriately. Following Percoco [21], we adopt the Probit model in the first regression:
(1)PY=1X=eX′β1+eX′β,where X is the independent variable and β is the regression coefficient. In this regression model, Green PPP project experience (SuccessPPI or NoSuccessPPI), type/subtype of PPI (Type_level and SubType_level), sponsors (DummySponsors), investment (Investment), institution quality (Corruption, Voice, and Stability), and other control variables (e.g., Income and L_Open) are independent variables. The dependent variable is project status (Status). If the project status of a Green PPP project is “concluded,” Status takes the value of 1 and 0 if the project status is “distressed” or “cancelled.” Table 6 describes the variables statistics in regression model 1.Table 6
Summary statistics of variables in regression model 1.
VariableObservationMeanStd. devMinimumMaximumStatus790.2420.3350.0001.000SuccessPPI791.6121.8810.0007.000NoSuccessPPI792.0122.1150.0005.000DummySponsors790.3120.4370.0001.000Investment79104.666397.0820.0002,990.883Type_level791.9260.8941.0004.000Subtype_level795.3203.9781.00012.000Voice79-0.1090.302-1.9720.402Anti_Corruption79-0.6920.298-1.0840.320Stability79-0.9080.569-2.2630.414L_Open7911.13111.7551.22884.198Income791.6580.7321.0003.000


## 3.3.2. Regression Model 2: Factors Influencing Green PPP Projects’ Internal Risk Structures

Regression model 1 reveals which factors will contribute to the success of a Green PPP project. Among them, we focus on the influence of internal risk structures. Then, the next question is, which factors affect a Green PPP project’s internal risk structure? Which factors affect private capital’s risk-taking degree in a Green PPP project? To answer these questions, we adopted cross-section regression model 2 to analyze factors influencing a Green PPP project’s internal risk structure. The advantage of cross-section model is that it can effectively solve the problem of insufficient sample size, estimate the impact of some factors that are difficult to measure on the explained variables, and help correctly understand the relationship between economic variables.(2)RiskSharing=α+∑i=1mηi⋅QIt+γ⋅Multilender+∑j=1nσj⋅Propj+∑I=1qθt⋅ControlI+ξ,where RiskSharing is the dependent variable representing private capital’s risk-taking degree in a Green PPP project. Private capital’s different risk-taking degrees in Green PPP projects are given by different contract arrangements of Green PPP projects, represented by variables Type_level and SubType_level. In regression model 2, the independent variables include institution quality (Corruption, Voice, and Stability), multilateral support (Multilender),Propj (DummySponsors, Investment, and Period),ControlI (L_Open, i_Low_Income, i_Lower_Middle_Income, i_Upper_Middle_Income, Ln_L_GDP_Growth, and i_Year). Regression model 2 only uses successful projects as research samples. Table 7 describes the variables’ statistics in regression model 2.Table 7
Summary statistics of variables in regression model 2.
VariableObservationMeanStd. devMinimumMaximumType_level5131.4260.5131.0004.000Subtype_level5135.0881.8181.00012.000Voice513-0.5240.767-1.8750.463Anti_Corruption513-0.6890.254-1.4650.547Stability513-0.9730.407-2.4401.209Multilender5130.0210.1670.0001.000DummySponsors5130.2260.4620.0001.000Investment513649.5591,108.2400.0004,093.656Period51325.47012.4831.00099.000L_Open51315.19813.0130.762109.427I_Year5134.3043.1821.00012.000i_Lower_Middle_Income5130.4020.3830.0001.000i_Upper_Middle_Income5130.4470.3940.0001.000Ln_L_GDP_Growth5132.9841.480-1.5005.500


## 4. Regression Result Analysis

### 4.1. Which Factors Determine the Success or Failure of a Green PPP Project?

Table8 presents results of regression model 1. In column (1), we empirically tested the effect of Green PPP project experience (SuccessPPI or NoSuccessPPI), subtype of PPI (SubType_level), sponsors (DummySponsors), investment (Investment), institution quality (Corruption, Voice, and Stability), and other control variables (L_Open and i_Income) on new Green PPP projects’ success. We found that subtype of PPI (SubType_level) has a statistically negative effect on Green PPP project success at the 5% significance level, meaning higher risk-taking degree of private capital in a Green PPP project increases the likelihood of project failure. Institution quality (Corruption, Voice, and Stability) and other variables (SuccessPPI, NoSuccessPPI, DummySponsors, L_Open, and i_Income) do not have a statistically significant impact on a Green PPP project’s success.Table 8
Results of regression model 1: factors determining the success or failure of a Green PPP project.
Variable(1)(2)(3)(4)(5)SuccessPPI0.030610.00562∗∗0.00424∗∗0.0003050.00914∗∗(0.0304)(0.00218)(0.00172)(0.00164)(0.00406)NoSuccessPPI-0.7378-0.1824∗∗∗-0.1108∗∗∗-0.0775∗∗-0.3111∗∗(0.553)(0.0579)(0.0411)(0.0341)(0.1251)DummySponsors-0.865-0.789-0.767(0.759)(0.507)(0.538)Investment-12.49-3.298∗∗-3.058∗∗∗-4.699∗(8.825)(1.442)(0.758)(2.426)Subtype_level-0.467∗∗-0.211∗∗∗-0.147∗-0.306∗∗-0.218∗∗(0.204)(0.0573)(0.797)(0.118)(0.113)Voice-7.438-0.708-0.663-1.359(5.358)(0.509)(0.623)(0.923)Anti_Corruption7.0890.6160.7350.845(4.561)(0.711)(1.041)(1.447)Stability5.6560.224-0.07161.141(4.109)(0.673)(0.664)(0.817)Voice∗Subtype_level0.0491-0.252(0.0889)(0.195)Corruption∗Subtype_level-0.211-0.0410(0.274)(0.253)Stability∗Subtype_level0.06780.152(0.152)(0.153)L_Open-0.198-0.0203(0.158)(0.0159)i_Income-5.725-0.522(4.796)(0.445)Constant20.222.2902.8191.2621.039(14.63)(0.609)(1.665)(0.772)(1.527)Observation7979797979R20.7460.5980.6380.4950.597Notes:∗∗∗,∗∗,and∗ indicate the significance levels of 1%, 5%, and 10%, respectively.In column (2), we remove these control variables and conduct regression considering a correlation between the control variables and institution quality. We found that subtype of PPI (SubType_level) has a statistically negative effect on new Green PPP projects’ success at the 1% significance level, Green PPP project experience (SuccessPPI) has a statistically positive effect at the 5% significance level, and Green PPP project experience (NoSuccessPPI) has a statistically negative effect at the 1% significance level. Investment (Investment) has a statistically negative effect at the 5% significance level, and institution quality (Corruption, Voice, and Stability) does not have a statistically significant impact.In column (3), we remove institution quality (Corruption, Voice, and Stability) from the regression. We found that Green PPP project experience (SuccessPPI) has a statistically positive effect on a new Green PPP project’s success at the 5% significance level, Green PPP project experience (NoSuccessPPI) has a statistically negative effect at the 1% significance level, investment (Investment) has a statistically negative effect at the 1% significance level, and subtype of PPI (SubType_level) has a statistically negative effect at the 10% significance level.Through the above three regression results, we can find that Hypothesis1 and Hypothesis 2 are both accepted. And the control variables also play a corresponding role in the regression model. To further study institution quality’s influence on a Green PPP project’s success, we introduced interaction variables between the institution quality and subtype of PPI variables (Voice∗Subtype_level, Corruption∗Subtype_level, and Stability∗Subtype_level). In column (4) regression, we added institution quality (Corruption, Voice, and Stability), subtype of PPI (SubType_level), and their interaction variables (Voice∗Subtype_level, Corruption∗Subtype_level, Stability∗Subtype_level), and we removed other control variables from the regression. We found that subtype of PPI (SubType_level) has a statistically negative effect on a Green PPP project’s success at the 5% significance level, while institution quality (Corruption, Voice, and Stability) and the interaction variables (Voice∗Subtype_level, Corruption∗Subtype_level, and Stability∗Subtype_level) do not have a statistically significant impact. Such regression results show that institution quality does not directly influence Green PPP project success, and the influences of institution quality and Green PPP projects’ internal risk structure are not affected by each other.In column (5) regression, we added control variables with a significant influence in the previous regression. Thus, institution quality (Corruption, Voice, and Stability) and the interaction variables (Voice∗Subtype_level, Corruption∗Subtype_level, and Stability∗Subtype_level) do not have a statistically significant impact on Green PPP project success. The regression results show that, although scholars deem that institution quality affects Green PPP project’s success, in our empirical test samples, institution quality’s influence on a Green PPP project’s success is not expressed.


### 4.2. Which Factors Determine the Internal Risk Structure of a Green PPP Project?

Regression model 2 tests factors that influence private capital’s risk-taking degree in a Green PPP project, namely, factors influencing Green PPP projects’ internal risk structure (Type_level and SubType_level). The full sample contains 513 Green PPP projects with project status of concluded, operational, and construction. We conducted regression in two subindustries: clean energy and sewage. The result of regression model 1 is presented in Table9.Table 9
Results of regression model 2: factors determining the internal risk structure of a Green PPP project.
VariableFull sampleEnergySewageVoice-0.586∗∗∗-0.0227-0.182(0.146)(0.161)(0.310)Anti_Corruption0.867∗∗-0.0479-0.182(0.397)(0.403)(1.173)Stability-0.1680.204-0.173(0.183)(0.177)(0.428)Multilender0.520∗∗0.407∗∗0.420(0.246)(0.186)(0.573)DummySponsors0.1260.221∗∗0.119(0.107)(0.162)(0.141)Investment0.0362∗∗∗0.00107-0.0127(0.0112)(0.0153)(0.162)Period0.0318∗∗∗0.003610.0601∗∗∗(0.00488)(0.00576)(0.0161)L_Open0.0129∗0.0136∗∗∗-0.0155(0.00711)(0.00490)(0.0178)I_Year-0.0103-0.0307∗∗-0.0762∗∗(0.0166)(0.0102)(0.0303)I_Lower_Middle_Income-1.365∗∗∗-1.011∗∗∗1.054∗(0.305)(0.203)(0.692)I_Upper_Middle_Income-1.849∗∗∗-0.778∗0.840(0.494)(0.499)(0.899)Ln_L_GDP_Growth-0.582∗∗∗-0.609∗∗∗0.296(0.163)(0.148)(0.366)Constant5.203∗∗∗6.344∗∗∗-1.344(1.226)(1.205)(1.668)Observation513312201R20.1470.1910.299Notes:∗∗∗,∗∗,and∗ indicate the significance levels of 1%, 5%, and 10%, respectively.From the full sample regression results, the degree of democracy in a country (Voice) has a statistically negative effect on private capital’s risk-taking degree in a Green PPP project (Subtype_level) at the 1% significance level. Thus, the more democratic is a country, the less willing is private capital to take risks in its Green PPP projects. The government’s ability to control corruption (Anti_Corruption) has a statistically positive effect on private capital’s risk-taking degree in a Green PPP project (Subtype_level) at the 5% significance level. Thus, the Hypothesis3 is accepted and a country can increase private capital’s willingness to participate in Green PPP projects by improving its ability to control corruption. Since Anti_Corruption is highly positively correlated with Law, Effectiveness, and Regulation, the above result also means that if a country has high degree to compliance with laws, high-quality public services, and high ability to set private capital development policies, private capital would be willing to take more risks in its Green PPP projects. Multilateral financial institutions involved in Green PPP projects (Multilender) have a statistically positive effect on private capital’s risk-taking degree in a Green PPP project (Subtype_level) at the 5% significance level. The regression results show that the Hypothesis 4 is accepted and it implies that the private capital tends to participate in the Green PPP project which involves multilateral financial institution. A Green PPP project’s investment and period of (Investment and Period) have a statistically positive effect on private capital’s risk-taking degree in a Green PPP project (Subtype_level) at the 1% significance level. The control variables (L_Open, i_Low_Income, i_Lower_Middle_Income, i_Upper_Middle_Income Ln_L_GDP_Growth, and i_Year) all have a statistically positive effect on private capital’s risk-taking degree in a Green PPP project (Subtype_level).There are differences in the regression results of the two subindustries (clean energy and sewage). Multilateral financial institutions’ involvement (Multilender) and private capital investors (DummySponsors) positively influences private capital participation in the clean energy sector’s Green PPP projects, while the influence on sewage sector is relatively weak. The main reason is that Green PPP project investment in the clean energy sector is large, and participation of multilateral financial institutions and multiple private capital investors can increase risk. On the contrary, the Green PPP project’s period (Period) positively influences private capital participation in Green PPP projects in the sewage sector, while the influence on the clean energy sector is relatively weak. This is because Green PPP projects’ period in the sewage sector is long; the risk-sharing mechanism is particularly sensitive to Green PPP projects’ period.


### 4.3. Robustness Test

The multicolumn regression in Tables8 and 9 has the effect of a robustness test. To further verify the models’ robustness, we replaced Subtype_level with Type_level in regression models 1 and 2 and found no changes in the conclusion. We also used Corruption, Democratic Accountability, and Government Stability from the ICRG database to replace the Anti_Corruption, Voice, and Stability in both regression models: the regression result did not change substantially.


## 4.1. Which Factors Determine the Success or Failure of a Green PPP Project?

Table8 presents results of regression model 1. In column (1), we empirically tested the effect of Green PPP project experience (SuccessPPI or NoSuccessPPI), subtype of PPI (SubType_level), sponsors (DummySponsors), investment (Investment), institution quality (Corruption, Voice, and Stability), and other control variables (L_Open and i_Income) on new Green PPP projects’ success. We found that subtype of PPI (SubType_level) has a statistically negative effect on Green PPP project success at the 5% significance level, meaning higher risk-taking degree of private capital in a Green PPP project increases the likelihood of project failure. Institution quality (Corruption, Voice, and Stability) and other variables (SuccessPPI, NoSuccessPPI, DummySponsors, L_Open, and i_Income) do not have a statistically significant impact on a Green PPP project’s success.Table 8
Results of regression model 1: factors determining the success or failure of a Green PPP project.
Variable(1)(2)(3)(4)(5)SuccessPPI0.030610.00562∗∗0.00424∗∗0.0003050.00914∗∗(0.0304)(0.00218)(0.00172)(0.00164)(0.00406)NoSuccessPPI-0.7378-0.1824∗∗∗-0.1108∗∗∗-0.0775∗∗-0.3111∗∗(0.553)(0.0579)(0.0411)(0.0341)(0.1251)DummySponsors-0.865-0.789-0.767(0.759)(0.507)(0.538)Investment-12.49-3.298∗∗-3.058∗∗∗-4.699∗(8.825)(1.442)(0.758)(2.426)Subtype_level-0.467∗∗-0.211∗∗∗-0.147∗-0.306∗∗-0.218∗∗(0.204)(0.0573)(0.797)(0.118)(0.113)Voice-7.438-0.708-0.663-1.359(5.358)(0.509)(0.623)(0.923)Anti_Corruption7.0890.6160.7350.845(4.561)(0.711)(1.041)(1.447)Stability5.6560.224-0.07161.141(4.109)(0.673)(0.664)(0.817)Voice∗Subtype_level0.0491-0.252(0.0889)(0.195)Corruption∗Subtype_level-0.211-0.0410(0.274)(0.253)Stability∗Subtype_level0.06780.152(0.152)(0.153)L_Open-0.198-0.0203(0.158)(0.0159)i_Income-5.725-0.522(4.796)(0.445)Constant20.222.2902.8191.2621.039(14.63)(0.609)(1.665)(0.772)(1.527)Observation7979797979R20.7460.5980.6380.4950.597Notes:∗∗∗,∗∗,and∗ indicate the significance levels of 1%, 5%, and 10%, respectively.In column (2), we remove these control variables and conduct regression considering a correlation between the control variables and institution quality. We found that subtype of PPI (SubType_level) has a statistically negative effect on new Green PPP projects’ success at the 1% significance level, Green PPP project experience (SuccessPPI) has a statistically positive effect at the 5% significance level, and Green PPP project experience (NoSuccessPPI) has a statistically negative effect at the 1% significance level. Investment (Investment) has a statistically negative effect at the 5% significance level, and institution quality (Corruption, Voice, and Stability) does not have a statistically significant impact.In column (3), we remove institution quality (Corruption, Voice, and Stability) from the regression. We found that Green PPP project experience (SuccessPPI) has a statistically positive effect on a new Green PPP project’s success at the 5% significance level, Green PPP project experience (NoSuccessPPI) has a statistically negative effect at the 1% significance level, investment (Investment) has a statistically negative effect at the 1% significance level, and subtype of PPI (SubType_level) has a statistically negative effect at the 10% significance level.Through the above three regression results, we can find that Hypothesis1 and Hypothesis 2 are both accepted. And the control variables also play a corresponding role in the regression model. To further study institution quality’s influence on a Green PPP project’s success, we introduced interaction variables between the institution quality and subtype of PPI variables (Voice∗Subtype_level, Corruption∗Subtype_level, and Stability∗Subtype_level). In column (4) regression, we added institution quality (Corruption, Voice, and Stability), subtype of PPI (SubType_level), and their interaction variables (Voice∗Subtype_level, Corruption∗Subtype_level, Stability∗Subtype_level), and we removed other control variables from the regression. We found that subtype of PPI (SubType_level) has a statistically negative effect on a Green PPP project’s success at the 5% significance level, while institution quality (Corruption, Voice, and Stability) and the interaction variables (Voice∗Subtype_level, Corruption∗Subtype_level, and Stability∗Subtype_level) do not have a statistically significant impact. Such regression results show that institution quality does not directly influence Green PPP project success, and the influences of institution quality and Green PPP projects’ internal risk structure are not affected by each other.In column (5) regression, we added control variables with a significant influence in the previous regression. Thus, institution quality (Corruption, Voice, and Stability) and the interaction variables (Voice∗Subtype_level, Corruption∗Subtype_level, and Stability∗Subtype_level) do not have a statistically significant impact on Green PPP project success. The regression results show that, although scholars deem that institution quality affects Green PPP project’s success, in our empirical test samples, institution quality’s influence on a Green PPP project’s success is not expressed.


## 4.2. Which Factors Determine the Internal Risk Structure of a Green PPP Project?

Regression model 2 tests factors that influence private capital’s risk-taking degree in a Green PPP project, namely, factors influencing Green PPP projects’ internal risk structure (Type_level and SubType_level). The full sample contains 513 Green PPP projects with project status of concluded, operational, and construction. We conducted regression in two subindustries: clean energy and sewage. The result of regression model 1 is presented in Table9.Table 9
Results of regression model 2: factors determining the internal risk structure of a Green PPP project.
VariableFull sampleEnergySewageVoice-0.586∗∗∗-0.0227-0.182(0.146)(0.161)(0.310)Anti_Corruption0.867∗∗-0.0479-0.182(0.397)(0.403)(1.173)Stability-0.1680.204-0.173(0.183)(0.177)(0.428)Multilender0.520∗∗0.407∗∗0.420(0.246)(0.186)(0.573)DummySponsors0.1260.221∗∗0.119(0.107)(0.162)(0.141)Investment0.0362∗∗∗0.00107-0.0127(0.0112)(0.0153)(0.162)Period0.0318∗∗∗0.003610.0601∗∗∗(0.00488)(0.00576)(0.0161)L_Open0.0129∗0.0136∗∗∗-0.0155(0.00711)(0.00490)(0.0178)I_Year-0.0103-0.0307∗∗-0.0762∗∗(0.0166)(0.0102)(0.0303)I_Lower_Middle_Income-1.365∗∗∗-1.011∗∗∗1.054∗(0.305)(0.203)(0.692)I_Upper_Middle_Income-1.849∗∗∗-0.778∗0.840(0.494)(0.499)(0.899)Ln_L_GDP_Growth-0.582∗∗∗-0.609∗∗∗0.296(0.163)(0.148)(0.366)Constant5.203∗∗∗6.344∗∗∗-1.344(1.226)(1.205)(1.668)Observation513312201R20.1470.1910.299Notes:∗∗∗,∗∗,and∗ indicate the significance levels of 1%, 5%, and 10%, respectively.From the full sample regression results, the degree of democracy in a country (Voice) has a statistically negative effect on private capital’s risk-taking degree in a Green PPP project (Subtype_level) at the 1% significance level. Thus, the more democratic is a country, the less willing is private capital to take risks in its Green PPP projects. The government’s ability to control corruption (Anti_Corruption) has a statistically positive effect on private capital’s risk-taking degree in a Green PPP project (Subtype_level) at the 5% significance level. Thus, the Hypothesis3 is accepted and a country can increase private capital’s willingness to participate in Green PPP projects by improving its ability to control corruption. Since Anti_Corruption is highly positively correlated with Law, Effectiveness, and Regulation, the above result also means that if a country has high degree to compliance with laws, high-quality public services, and high ability to set private capital development policies, private capital would be willing to take more risks in its Green PPP projects. Multilateral financial institutions involved in Green PPP projects (Multilender) have a statistically positive effect on private capital’s risk-taking degree in a Green PPP project (Subtype_level) at the 5% significance level. The regression results show that the Hypothesis 4 is accepted and it implies that the private capital tends to participate in the Green PPP project which involves multilateral financial institution. A Green PPP project’s investment and period of (Investment and Period) have a statistically positive effect on private capital’s risk-taking degree in a Green PPP project (Subtype_level) at the 1% significance level. The control variables (L_Open, i_Low_Income, i_Lower_Middle_Income, i_Upper_Middle_Income Ln_L_GDP_Growth, and i_Year) all have a statistically positive effect on private capital’s risk-taking degree in a Green PPP project (Subtype_level).There are differences in the regression results of the two subindustries (clean energy and sewage). Multilateral financial institutions’ involvement (Multilender) and private capital investors (DummySponsors) positively influences private capital participation in the clean energy sector’s Green PPP projects, while the influence on sewage sector is relatively weak. The main reason is that Green PPP project investment in the clean energy sector is large, and participation of multilateral financial institutions and multiple private capital investors can increase risk. On the contrary, the Green PPP project’s period (Period) positively influences private capital participation in Green PPP projects in the sewage sector, while the influence on the clean energy sector is relatively weak. This is because Green PPP projects’ period in the sewage sector is long; the risk-sharing mechanism is particularly sensitive to Green PPP projects’ period.


## 4.3. Robustness Test

The multicolumn regression in Tables8 and 9 has the effect of a robustness test. To further verify the models’ robustness, we replaced Subtype_level with Type_level in regression models 1 and 2 and found no changes in the conclusion. We also used Corruption, Democratic Accountability, and Government Stability from the ICRG database to replace the Anti_Corruption, Voice, and Stability in both regression models: the regression result did not change substantially.


## 5. Conclusion

Our study used the dataset of 552 Green PPP projects to analyze the effects of institution quality and multilateral financial institutions on Green PPP projects’ results (success or failure). We focused on African countries that have huge Green PPP projects demands for green infrastructure construction. We show that the institution quality of a Green PPP project location leads private capital to take more risks in the Green PPP project and indirectly influences the project’s results. Our study is the first to connect the institution quality with the result of Green PPP projects through its internal risk structure.Our analysis used a Probit regression model to estimate factors influencing a Green PPP project’s success or failure. We found that a country with successful Green PPP project experience is associated with an increase in success rate when launching a new Green PPP project, and private capital’s risk-taking degree in a Green PPP project negatively influences Green PPP project success. However, institution quality has no statistically significant impact on Green PPP project success. To explore the role of institution quality, we introduced interaction variables between the institution quality and subtype of PPI variables into the regression model. We found that although scholars deem that institution quality influence the success of a Green PPP project, this influence is not seen directly in our empirical test samples.Then, we used a cross-section regression model to estimate factors affecting Green PPP projects’ internal risk structure. The regression results showed that institution quality has a mixed effect on Green PPP projects’ internal risk structure. Voice has a negative impact on private capital’s risk taking in a Green PPP project. This implies that concentration of government resources and power can, to a certain extent, increase the government’s mobilization and action power, reduce uncertain risks and transaction costs of Green PPP projects, and create greater incentives for private capital. This will promote private capital’s participation in Green PPP projects to a greater extent in African countries.The regression results also found that multilateral financial institutions’ participation can affect the internal risk structure of Green PPP projects. This implies that multilateral financial institutions can urge governments to ensure Green PPP projects progress smoothly and reduce the risk in private capital’s participation in Green PPP projects.



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*Source: 3825211-2022-06-30.xml*