Literature on the effect of religion and religiosity on corruption has failed to distinguish between direct and mediated effect and to determine the different roles of two alternative mediation channels—the personal versus the cultural channel. Using a data set of 102 countries, we found that hierarchical religions have significant positive associations with corruption levels, while the non-hierarchical Protestant Christianity has significant negative associations with corruption levels. Most of the effect is mediated by the cultural channel. However, some hierarchical religions (particularly Sunni Islam and Orthodox Christianity) have both an indirect mediated effect and a direct unmediated effect, suggesting an additional dissociative parameter besides the hierarchical/non-hierarchical parameter, which we suggest is the status of a formal/informal state religion. The findings are closely linked to the modern view of economic development that although institutions give rise to economic growth, it is culture that shapes institutions. AccordiReligion plays a significant role in influencing corruption levels . While aRecent research suggests that religious devotion, or religiosity, is associated with higher levels of corruption, in addition to the effect of each religion ,38. WhilThe existing literature on the effects of religion on corruption faces several issues. First, most studies fail to distinguish between the dominant religion and the percentage of each religion in a given population. Therefore, it remains unclear whether membership in minority religions is associated with the same level of corruption as in countries where these religions are dominant. This issue could help identify the channel that confers the associations between religion and corruption. If, indeed, members of minority religions are associated with the level of corruption characterized by the dominant religion (and not with the level of corruption in countries in which their religion is dominant), it may be a country's general religious norms that affect its culture and that these norms are distributed not only to the dominant religion's members . On the other hand, if members of minority religions are associated with the levels of corruption within countries in which their religions are dominant; this may be the effect of the religious norms that are distributed only to the religion's members . In fact, there are indications that the dominant religion may culturally affect members of minority religions, such as the effect of different religions on entrepreneurship 41,42], w,42, w41,Second, the literature has not resolved whether the cultural effect of religions on corruption is transmitted through institutions, norms, or both. Although hierarchical religions were associated with high corruption levels, it is unclear whether the effect is transmitted only through the role of institutions , or the result of normative transmitters .Third, recent research found that religiosity is associated with higher corruption levels, possibly due to personal channels . HoweverTo address these issues, we analyzed a dataset of 102 countries, covering 89% of the world's population, and posit that the cultural channel supersedes the personal channel, and suggest that members of minority religions adopt the ethical conduct of the dominant religion, even after controlling for a large set of institutional and normative variables. Furthermore, we argue that hierarchical religions contribute to high levels of corruption both directly and through their effect on institutions and norms. Similarly, we argue that formal or informal state religions also contribute directly to high levels of corruption. Finally, we find that religiosity has an additional contribution to corruption levels through its interconnectivity with hierarchical religions, especially hierarchical state religions. Overall, our findings support the cultural over the personal channel view, which is closely linked to the modern view of economic development which posits that although institutions give rise to economic growth, it is culture that shapes institutions.This paper makes several significant theoretical contributions to the existing literature. First, it examines the role of minority religions, allowing for a comparison between the cultural effect of the country's dominant religion and that of minority religion. Second, it accesses the different roles of the cultural and personal channels in the relationship between religion, religiosity, and corruption. Third, it is the first study, to the best of our knowledge, to isolate the roles of religiosity, hierarchical religions, and state religions, which can resolve the internal contradiction between advocating for better moral conduct associated with religiosity and the association of religiosity with corruption. It also addresses the difference between the association of the non-state hierarchical Catholic religion and corruption versus that of state hierarchical religions and corruption. Thus, this study offers a revised theoretical model of religion, religiosity, and corruption.The rest of this paper is structured as follows. In Section 2Religions are often associated with advocating for better moral conduct and improved ethical behavior at a personal level ,33. FurtHowever, the relationship between religiosity and corruption is more complex. For example,  found thHypothesis (1)H1: The impact of the dominant majority religion dummy variable on the level of corruption scores will be stronger and more significant than the impact of the variable of the percentage of a specific religion in the population when estimating the association of specific religions with the level of corruption.To develop a more comprehensive understanding of the relationship between religion and corruption, it is essential to distinguish between the personal and cultural channels. While an inverse relationship between religiosity and corruption might be expected, if the personal channel were the only way religion affected society, previous studies have demonstrated otherwise . To dete2.1Recent research has indicated that countries with hierarchical religions tend to have higher levels of corruption than countries with non-hierarchical religions ,30,36. THypothesis (2a)H2a: When hierarchical religions are characterized as dominant majority religions, they are positively associated with corruption.Hypothesis (2b)H2b: In hierarchical religions, the level of religiosity in a country is positively associated with corruption.Furthermore, hierarchical religions tend to be less “democratic” than non-hierarchical religions, in the sense that there are no “competing” churches, and religious leaders hold more power . This co2.2However, the parameter of hierarchical religions is not the only factor that distinguishes world religions. Protestant Christianity, for example, is not only non-hierarchical; it is anti-hierarchical, as it is characterized by individualism and tends to monitor public officials ,43. ConsIslam was established as a ruling religion, in which its founder is the religious leader as well as the leader of the empire. Although the political leadership in Muslim countries was separated from the religious leadership throughout history, Islam still considered itself to be directing the state, and both the people and the political leaders cooperated with religious leaders to receive public support and acknowledgment. This unique relationship has led scholars to coin the term “political Islam” ,46. OrthIn fact, both Islam ,46 and OHypothesis (3a)H3a: The state religions, when characterized as dominant majority religions, are negatively associated with the level of corruption scores (positively associated with corruption).Hypothesis (3b)H3b: In state religions, the religiosity level in a country is negatively associated with the level of corruption scores (positively associated with corruption).Hypothesis (3c)H3c: In hierarchical state religions , the religiosity level's negative association with level of corruption scores is higher than in non-hierarchical state religions or in hierarchical non-state religions.Therefore, there may be a more complex relationship between hierarchical religions that are also a formal state religion (or at least considered an important element of the state) and corruption scores, in which rather than just an indirect correlation through the cultural channel, it will also have a direct link to corruption. We expect that this formal position allows hierarchical religions to have more active involvement in the public sector, but it may also weaken their willingness to monitor and criticize public corruption due to the intertwined institutions of local religions and states. Additionally, as mentioned earlier, a decrease in religious freedom and diversity is associated with an increase in corruption ,32,37. C2.3Religions not only transmit cultural effects through institutional differences but also through significant normative and sociocultural differences that can affect corruption. Research indicates that more religious individuals have a high level of trust in others , in the Attitudes toward “sins” and forgiveness also play a role. Protestant Christianity and Judaism believe that avoiding sins is the responsibility of individuals during their lives, while Catholicism may be viewed as more tolerant toward sins and the possibility of redemption . AdditioHypothesis (4a)H4a: The associations between hierarchical state religions , when characterized as dominant majority religions, and the level of corruption scores will be negatively significant even after controlling for norms and institutional transmitters.Hypothesis (4b)H4b: The negative associations between hierarchical and/or state religions, when characterized as dominant majority religions, and the level of corruption scores are significantly mediated by both the normative and institutional transmitters.To determine whether both normative and institutional differences contribute to different levels of corruption, we use a large set of normative and institutional controls and examine whether they significantly mediate the dominant religions’ contribution to corruption. Thus, we hypothesize the following.Our theoretical model is illustrated in 3The study sample comprises 102 countries, which account for 89% of the world population in 2020 .3.1Corruption is estimated using the Transparency International Corruption Perception Indexes (TI CPI) for the year 2020. The TI CPI ranks 180 countries based on how corrupt their public sectors are perceived to be. The index combines surveys and assessments of corruption collected by various reputable institutions, making it the most widely used corruption indicator worldwide. Measuring absolute levels of corruption in countries is challenging, given that corruption is primarily a hidden illegal activity. Thus, assessing the relative levels of corruption across countries by capturing perceptions about corruption is the most reliable method. The TI CPI provides an expert-based perception of corruption levels that may influence investors and consumer behavior.To assess the robustness of the results, we also used the World Bank's Worldwide Governance Indicators (WGI) corruption control rank as a complementary measure. The WGI corruption control rank measures the perceived level of corruption based on data collected from a variety of sources. The WGI is produced by the World Bank and provides a range of governance indicators for over 200 countries. The corruption control rank measures the extent to which public power is exercised for private gain, including both petty and grand forms of corruption. The rank is based on a composite score that takes into account multiple data sources, including surveys of experts, businesspeople, and citizens, as well as assessments by international organizations and other publicly available information. The corruption control rank ranges from 0 to 100, with higher scores indicating a lower perceived level of corruption. It is important to note that the WGI corruption control rank is a perception-based measure, and therefore may not reflect actual levels of corruption in a country. Additionally, the data used to calculate the rank may be subject to biases and limitations. Despite these limitations, the WGI corruption control rank remains a widely used tool for comparing corruption levels across countries and tracking changes over time.Despite similarities between the two measures, the CPI score and the WGI corruption control rank differ in some respects. The CPI relies solely on expert assessments and opinion surveys of businesspeople and analysts from around the world, while the WGI incorporates a wider range of data sources, including citizen surveys and assessments by international organizations. Additionally, while the CPI focuses specifically on corruption in the public sector, the WGI corruption control rank measures the perceived level of corruption more broadly, including both petty and grand forms of corruption.3.2The purpose of this study is to investigate the effect of religion on corruption utilizing three sets of variables. First, a dummy variable is used to identify the primary religion of each country, including the eight major religions . Additionally, a dummy variable is added to represent countries where the primary religion is not one of these major religions, primarily African Christian countries and Pagan countries. Second, the study examines the percentage of the population belonging to each of the eight major religions, as well as the percentage of individuals who do not belonging to any religion. Data for the first two sets of variables were obtained from the CIA Factbook.The third set of variables concerns the level of religiosity, and data were obtained from three sources. First, the World Values Survey (WVS) provided data for the seventh wave for 79 countries as of December 31, 2021, and 14 additional countries for the sixth wave. Second, the Gallup International Association published data at the end of 2016 for nine countries (out of 66) not included in the WVS. The level of religiosity was estimated using ten questions from the WVS survey, which measured the importance of religion in people's lives. These questions were used to create a total religion grade, adjusting the scales to measure the more religious side. They included questions such as V9 ; V25 ; V145 ; and V152 (“How important is God in your life?“). The survey used different Likert scales and “yes-or-no” questions. Combining the 10 questions allowed for a single total religion importance grade. A factor analysis for the 10 questions was conducted, revealing only one significant factor associated with all of the items. Results indicated a high reliability of the estimated total religion importance grade, as indicated by the very high Cronbach's α of the entire questionnaire (0.945). The total religion grade of the 14 countries obtained from WVS6 and the nine countries obtained from Gallup was estimated using a similar procedure of incorporating questions related to religion into a single grade. Fixed effects dummy variables for each data set were included in each specification.3.3The study includes control variables based on previous research on the macroeconomic drivers of corruption. These variables consist of economic freedom, democracy index, government expenditure on education, urbanization, population growth rate, GDP (PPP) per capita, gender discrimination parameters, the level of trust in other people, and dummy variables indicating countries with a colonial or socialist history, which are associated with corruption. The level of economic freedom and the democracy index were included because centralized decision making and the concentration of power are potential causes of corruption, while greater market competition reduces corruption ,16,58. GFurthermore, we included several normative and cultural variables, such as gender discrimination parameters  and the level of trust in other people, as culture has also been discussed in the literature as a possible source of corruption ,63. Some3.4Although our data set allowed us to examine 89% of the world population, it only incorporated 102 out of 195 recognized countries. Notably, most of the excluded countries are small in size, which introduces bias towards larger countries and limits our ability to investigate differences between very small and larger countries. Nonetheless, we controlled for both GDP and population in our specifications to account for potential confounding factors.Furthermore, our data sources did not enable us to examine changes over time. Although the scope of the WVS Survey has increased over time, we still needed to use two waves of the WVS survey and the Gallup International Association data set to build our sample. Using previous waves to build data for longitudinal research would have significantly limited our sample. We faced similar challenges with the corruption data sources. However, we believe that the inability to consider changes over short to medium terms is not crucial. This is because corruption, and by no means religion, tends to be highly stable according to the literature . In fact4To test our hypotheses, we conducted four sets of regressions. The first set of regressions a examineTo support We tested associations between specific religions and levels of corruption using the eight major religions (A1 and A5), while controlling for the country's religiosity level (A2 and A6). We removed statistically insignificant religion variables in further specifications (A3 and A7), and the final specifications, we used specific religious variables multiplied by religiosity level (A4 and A8). Our results were consistent across all specifications and supported The variables capturing the percentage share of Catholic Christians, Orthodox Christians, Hindus, Buddhists, and Jews in the population were found to be statistically insignificant both before and after controlling for religiosity level  were negatively significant in the uncontrolled specifications , and the Protestant Christians majority dummy was positively associated with the corruption score (negatively with corruption). The majority dummy variables for Hindus and Buddhists were found to be statistically insignificant, and the Jews majority variable was excluded from the model as it was equal to 1 in only a single case.In the specifications that controlled for institutional and normative variables c, only tIn support of In support of In support of To examine In Internal differences between hierarchical religions are evident in their effects on corruption. Specifically, Orthodox Christianity, Sunni Islam, and Shia Islam have a direct effect on corruption, whereas Catholicism only has an indirect effect, which is mediated through institutional and normative control variables  in their respective countries. This association operates through their dominant majority religious status , rather than through the percentage of people in the country belonging to specific religions . Three of the four hierarchical religions had both direct and indirect associations, while Catholicism had a lower corruption score (higher level of corruption) only in the uncontrolled specification and was characterized only by an indirect association through the institutional and normative variables. Anti-hierarchical Protestant Christianity was associated with higher corruption scores (lower levels of corruption), but the full effect was mediated by the institutional and normative variables. The study shows that most of the positive and negative associations of hierarchical and non-hierarchical religions on corruption scores are mediated through the two cultural transmitters: norms and institutions.The findings are closely linked and are perfectly in line with both the corruption literature and the modern view of economic development literature. Numerous studies have stressed the relationship of institutions and corruption and especially the effective separation of powers, the freedom of the press to monitor the government, and need for transparency and accountability of government to tackle corruption. Other studies stress the role of cultural factors play in corruption, and especially in societies characterized by high of power distance and collectivism (see in more details the literature review section). In parallel, the modern view of economic development, which suggests that institutions give rise to economic growth , while cFurthermore, despite the inclusion of numerous control variables in the specifications, the impact of three out of four hierarchical religions on corruption scores appears to be directly associated, rather than solely mediated through the effect on institutions and norms. These findings indicate that the analysis of hierarchical versus non-hierarchical religions fails to provide a complete understanding of the issue at hand. The research highlights the significance of a missing parameter, i.e., state religion, where Catholicism and Protestant Christianity, as non-state religions, only exhibit an indirect impact, whereas three hierarchical state religions - Sunni Islam, Shia Islam, and Orthodox Christianity - display both direct and indirect associations with corruption scores. In contrast, two state non-hierarchical religions, Hinduism and Buddhism, do not exhibit any significant associations at all.The extensive control variables, particularly the economic and institutional variables, and the robustness of the results across alternative specifications, rule out potential alternative explanations or confounding factors. However, the cross-sectional nature of the dataset prevents us from distinguishing between cause and effect. Specifically, we cannot determine whether hierarchical religions in a state shaped cultural norms and institutions that influenced corruption or whether states adopted hierarchical religions that suited their needs during development. Although our results do not directly lead to conclusions, they provide a potential indication. Since many of the countries in our dataset are twentieth-century countries that adopted their religions centuries before, it is possible that religion is the cause and not the outcome. Future research could use more detailed data on corruption perceptions in areas that have switched religions, such as between Catholicism and Protestant Christianity or Sunni Islam and Shia Islam, to shed more light on this important topic. In addition, unfortunately, the data set does not allow exploring the characteristics of state religions that are behind the direct effect such as the level of direct involvement of religious leaders in state affairs, which should be left for future research.Regarding the personal channel, the study found that the presence of followers of these hierarchical religions in other countries or the presence of followers of non-hierarchical religions, did not correlate with changes in corruption scores. Moreover, recent literature has shown that the negative association between religiosity and corruption scores (positively correlated with levels of corruption) is biased due to omitted variables. Therefore, religiosity is not directly related to corruption scores, but only indirectly related. When religiosity is presented alone, regardless of the dominant religion in the country, it is always significantly negatively associated with corruption score (positively associated with higher levels of corruption). However, when religiosity is multiplied with the dominant majority religious dummies, it is only significantly negatively associated with corruption score for hierarchical and state religions. This suggests that as religiosity levels increase, the religion's members become less critical of the religious leaders and institutions. Thus, in institutional settings that incentivize corruption , the religiosity level has an indirectly negative impact on corruption score.This study highlights the liberal view of separating religion from the state for the benefit of both the state and religion. The close interaction between political and religious institutions creates too much concentration of power and decreases incentives to monitor both the government and the religious leaders, ultimately leading to corrupt countries and religious leaders and institutions that do not serve their members.The practical implications of this study are clear. Checks and balances are crucial in every country, but they are especially vital in countries that have a state hierarchical religion. Furthermore, it is crucial that followers of the state hierarchical religion are knowledgeable about the norms that affect corruption, as well as the transparency and efficacy of state institutions. To combat corruption effectively, a more involved public is needed, especially among followers of the majority religion, who should demand greater transparency in the use of public resources. International agencies that seek to help countries fight corruption should place greater emphasis on promoting transparency in the use of government resources and authority to both the public and the media. They should not rely solely on government self-action to tackle corruption.Both authors, Prof. Yaron Zelekha and Dr. Gil Avnimelech, equally contributed to all aspects of this paper.The author contributions are as follows:Conceived and designed the paper framing and theory: Prof. Yaron Zelekha and Dr. Gil Avnimelech jointly developed the research framing and theory.Collected the data: Prof. Yaron Zelekha and Dr. Gil Avnimelech both managed the data collection.Analyzed and interpreted the data: Prof. Yaron Zelekha and Dr. Gil Avnimelech closely collaborated in analyzing and interpreting the data. They performed statistical analyses together, interpreted the results, and drew meaningful conclusions.Wrote the paper: Prof. Yaron Zelekha and Dr. Gil Avnimelech shared equal responsibility in writing the manuscript. They collaborated on drafting the paper, organizing the content, and refining the language to ensure clarity and accuracy.Both authors have read and approved the final version of the manuscript and have agreed to be accountable for all aspects of the work, ensuring its integrity and accuracy.Data will be made available on request.This research was supported by grants from the research authority of Ono Academic College. None of these institutions create any conflict of interest.No additional information is available for this paper.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


The Property Assessed Clean Energy (PACE) Program is an innovative financing approach to meeting environmental goals. PACE financing is structured as an assessment to the property and paid along with the property tax bill. In addition to the direct environmental benefits, it also yields co-benefits of enhanced economic output and employment. This paper estimates the economic impacts of PACE in California by one of its major financing companies. These impacts include direct spending on structural improvements, reduction in spending on centralized power and water services, reallocation of spending from energy and water bills savings, and solar investment tax credits, among others. It also includes general equilibrium effects of these various factors. Our results indicate PACE financing yields sizable economic benefits. At the same time, the increased economic activity results in increased energy and water use that partially offsets some of the direct environmental gains. Furthermore, PACE has been subject to criticism because it gives financing companies the first lien on mortgages and because of anecdotal examples of some customers being lured by unscrupulous contractors. The direct environmental benefits and economic co-benefits of PACE should be factored into the policy debate over whether the Program should be further expanded or regulated. •Property Assessed Clean Energy provides innovative financing for environmental goals.•Ygrene Energy Fund has financed over 32,000 CA projects since 2013 for $760 million.•Reduced net electricity use by 3.53 million MWh and natural gas use by 2.02 bcf in CA.•Increased the NPV of GDP by $661 million and employment by 9,774 person-years in CA.•Economic co-benefits should be considered in discussions of regulation and expansion. Established by state statutes and enabled by local governments, PACE financing, unlike traditional alternatives, is an assessment to the property and not the property owner. Only certain types of improvements are eligible to be financed through PACE, and all PACE programs are required to provide public benefits in the host state. PACE provides one hundred percent of the cost of qualified structural improvements, which the property owner repays annually or semi-annually through a special assessment added to the property tax bill.co-benefits directly and indirectly. These benefits are more complex than those measured in ordinary economic impact analysis associated with typical economic stimuli such as the opening of a new factory or mine. Such stimuli, as well as PACE financing, all generate direct impacts on site and multiplier or broader general equilibrium impacts throughout the rest of the economy. However, many conventional stimuli provide products primarily for export and do not interact much with the host economy beyond a limited number of supply chain and wage/salary income increases and spending. PACE improvements, on the other hand, provide energy and water cost savings that stimulate spending on consumer goods and enhance business profits. At the same time, the efficiency improvements reduce the production of conventional energy and centralized water services and affect their prices. Hazard mitigation improvements help prevent the need to dip into savings to repair damaged structures and compensate for business interruption.The direct benefits of PACE financing are rather straightforward—reduction in energy and water use, reduction in pollutants associated with this use, and reduction in hazard vulnerability. However, the financing also generates economic This study performs a regional economic impact analysis of PACE financing by one of the leading firms in the market—Ygrene Energy Fund, Inc. Since 2013, and through the end of July 2018, Ygrene has provided more than $1.16 billion to finance over 54,500 property improvement projects in over 500 cities and counties in California, Florida, and Missouri . We estiThis paper fills an important void in the literature on the total regional economic, environmental, and resource use impacts of PACE financing. Most of the analyses of PACE financing have dealt with its strengths and limitations  that are not quantified in this study, add to the “business case”, “household benefits” and “overall societal well-being” of PACE financing.Between its start in 2013 through July 2018, Ygrene financed 31,867 residential properties and 646 commercial properties in California.33.1Estimating the economic impacts of the PACE Program involves a number of considerations. It includes several positive stimulus factors, as well as some partially offsetting ones. Some of these impacts relate to the buildings in which improvements are made, while others relate to direct and indirect impacts off-site. Many of the benefits of the PACE Program pertain to improving the environment and are complex to assess because of the absence of market prices associated with them.A set of positive stimulus factors that directly affect the economy stems from the purchase of materials and equipment, as well as the labor involved in their installation, for PACE improvements. Examples include energy-efficient water heaters, solar panels, windows, caulking material, and wages paid to workers with various skill levels. The demand for the various inputs  into the PACE improvements directly increase the economic activities of several sectors that produce these inputs, which leads to further upstream and downstream interindustry multiplier effects. The demand also leads to the payment of wages/salaries to workers and returns to capital to business owners, which they subsequently spend, setting off further rounds of multiplier effects. Additional positive stimuli emanate from the operation and maintenance of PACE improvements throughout their useful life.Another set of positive stimuli is associated with the savings from improved efficiency in the use of energy and water. Homeowners then use the major portion of these savings to purchase more of the typical basket of consumer goods, thereby generating additional economic activity. Business owners could use the savings for several purposes, including expanding production, lowering prices, or increasing wages and profits.Similar savings stem from mitigation efforts that reduce vulnerability to disasters. Here, however, the assessment of spending is more complicated, because, in the absence of these improvements, property owners would spend money anyway to repair and reconstruct damaged facilities. On the surface this could be an equivalent amount to spending on the improvement and would appear to be a wash in terms of direct economic impacts as measured by standard economic indicators such as gross output or employment. However, implementing disaster mitigation improves the level of well-being of the property owners by reducing their risk of loss of property or income, while repair and reconstruction simply returns well-being to the original level. The well-being of homeowners and businesses are measured in this report in terms of their expenditures on hazard mitigation.A set of direct substitution effects also needs to be taken into account, which offsets the aforementioned positive stimuli to a lesser, equal, or greater extent. Reduced spending on energy and water results in a decrease in the economic activity that produces them. The net effect is likely to be positive within most states, however, since the majority of the positive stimulus spending will increase the activity of in-state producers, while the inputs to displaced production are top-heavy with goods imported from other states .Additional positive stimuli stem from interest and fees paid to Ygrene and from fees paid to state and local governments for operating costs, including permitting and inspection. Fees paid to governments at various levels increase their revenues and are likely to be spent on government provision of goods and services within a state, thus generating an additional direct stimulus. Payments to Ygrene increase its business activity, and hence wages and profits, with the former subsequently spent primarily within the state.Yet another stimulus associated with PACE financing stems from the 30% solar energy federal investment tax credit (ITC). This payment essentially increases the disposable income of households and increases consumer expenditures on goods and services, on the one hand, and increases business profits, or possibly lowers prices, on the other. The credit is usually received by the property owners between 4 and 16 months after the installation of the solar energy system. We assume that the households will spend 50% of the credit dollars in the year of the installation, and 50% in the following year. Moreover, since the solar ITC is a federal tax incentive, we assume there will be no reduction in federal government spending in California or increase in other taxes in the state as an offset effect.Finally, we summarize some additional benefits and downsides of PACE financing, the estimation of which are beyond the scope of this study. There is evidence that the financed improvements increase the value of the home or business, in part because of the lower energy and water costs, as well as reduced vulnerability to hazards. A complex issue of the analysis is that the PACE Program provides assessments rather than outright grants. Since these assessments need to be repaid, this results in a diversion of expenditures from other goods and services by consumers or from production activities by businesses. Thus, with respect to the spending from and repayment of the assessments, there is a significant stimulus in the year(s) that the improvement is implemented, but a negative stimulus to the economy spread over the period during which the assessments are repaid. This repayment can come equivalently from prior savings or assets of the borrowers or from savings on the utility bills and insurance premiums that the improvement provides, or some combination of both.Even the initial use of the assessments may involve some offsetting factors depending on the origin of the capital base of a PACE Program lender. The assessment of the PACE Program is being done at the local level within a state, so, if the capitalization of the lender comes from funds from out-of-state, then there is an equivalent positive stimulus from all the assessments. However, if the capital comes from within the state, we must evaluate whether it displaces the use of these investment funds for other purposes. If so, this displacement effect would have to be taken into account, and the ensuing direct and indirect impacts would have to be subtracted from the bottom-line impacts. The difficulty in addressing this component is that firms that invest in PACE securitizations are active in global markets. While many firms have headquarters in the US, some in states where PACE programs operate, we do not have access to their geographical investment portfolio. Thus, estimating where funds originated would be extremely difficult if not impossible. Given the relatively small amount of PACE financing as a proportion of total global capital market funds available, we believe that an assumption of zero in-state investment displacement is reasonable.3.2A major set of direct impacts stems from reduced water use, reduced energy use, and reduced greenhouse gas emissions. Unfortunately, directly observable market prices do not exist for pollutants, so we will apply some measures in the literature of the damages they cause, where the benefits of PACE assessments represent these avoided losses. For example, we use the findings of a recent National Academies of Science report on the social cost of carbon 3; howeveIn the case of the benefits of decreased energy use and water use, we encounter two complexities. First, we could apply the value of these resources, but this would overstate the benefits because we have already counted them in the consumer savings. At the same time, simply using the values of these resources at market prices omits the consumer and producer surplus components and causes some underestimation. Hence, we simply present the amounts of energy and water savings in physical units.3.3As noted above, the spending on property improvements that are made possible by Ygrene financed local government assessments apply to the site of their application, or what is termed direct economic impacts. However, the estimation of the total, or macroeconomic, impacts includes the ripple, or multiplier, effects of the various increased or decreased spending streams, as well as the interaction of demand and supply in numerous markets. For example, the increased demand for high-efficiency water heaters directly stimulates the demand for inputs into their production, such as fabricated metals, insulation material, and labor. The process continues through a chain-reaction of indirect impacts, as more fabricated metal and insulation production stimulates demand for more of their inputs, and as the producers of these inputs demand more inputs, and so on.The many types of linkages in the economy and macroeconomic impacts are extensive and cannot be traced by a simple set of calculations. They require the use of a sophisticated model that reflects the major structural features of an economy, the workings of its markets, and the interactions between them. In this study, we used the Regional Economic Models, Inc. (REMI), Policy Insight Plus (PI+) Model . This is4A summary of the data used in this study is presented in Appendix Ygrene Energy Fund, Inc. provided us with extensive data on its financing from 2013 through July 2018. The data covered major characteristics of the individual assessments, including major assessment characteristics , financing characteristics , and characteristics of the property .The authors then mapped improvement expenditures to the 160 economic sectors in the REMI Model to calculate the indirect effects. This began by dividing these expenditures between sectors that produced/supplied the property improvement materials/equipment and installation of the equipment or retrofit of the structure.4Data on energy and water savings were obtained from the “Ygrene Proprietary Impact Metrics Model” developed by Ygrene. This impact model is used to estimate the energy savings, water savings, natural gas savings, renewable energy generation, utility bill savings, and the associated greenhouse gas emissions reductions. The impact model was based on a 12-month data-set of Ygrene funded projects between July 2017 and June 2018. For each residential and commercial property in the data set, Baseline Energy Consumption by end-use  and Baseline Water Consumption by end-use  were determined to form the foundation upon which improvement level savings are estimated.After the energy  and water savings quantities were estimated at the improvement level, state-level energy and water rates for residential and commercial sectors and emissions factors were applied to these savings quantities to generate associated utility bill savings and greenhouse gas reductions.All energy, water, utility bill, and greenhouse gas saving estimates were then divided by the total contract amount  within each of the improvement categories to derive the energy, water, utility bill, and greenhouse gas savings factors per thousand contract dollars by improvement type, state, and property type. These factors are then applied to the entire Ygrene portfolio based on the contract amounts for each improvement type, state, and property type to estimate annual savings.To estimate lifetime savings, annual factors are applied for the useful life of each improvement type. When calculating lifetime utility bill savings, it is assumed that the prices of electricity, natural gas, and water will increase or decrease at the average annual historical growth rates over the past 6 years in each state . AdditioMitigation Saves 2 Study  for the earthquake threats for each improvement type. The BCRs were obtained from results calculated as part of the  2 Study . Note thNote also that due to the uncertainty surrounding some of the data, below we perform sensitivity tests around best estimates of data inputs into the analysis.55.1The implementation of Ygrene PACE improvements is estimated to result in substantial direct benefits from energy consumption reductions and water conservation. In California, the energy/water efficiency and renewable energy PACE projects are estimated to lead to electricity consumption reductions of 3.63 million megawatt hours (MWh), natural gas consumption reductions of 2.86 billion cubic feet (bcf), and water savings of 2.36 billion gallons over the entire useful life of the improvements. 75.2State-specific emissions factors were applied to the energy saving quantities to calculate the associated greenhouse gas reductions.5.3Mitigation Saves 2 Study  of the PACE Program. It is the most widely used macroeconometric model to analyze the economic impact of energy and climate policies in the U.S. The REMI Model has evolved over the course of more than 30 years of refinement  model, i.e., it divides the economy into 160 sectors, and thereby depicts important distinctions among them.The REMI model is able to analyze the quantity interactions between sectors (ordinary multiplier effects) but with refinements for price changes not found in I–O models. That is, the Model incorporates the responses of producers and consumers to price signals and the changes in other market and regulatory conditions, and captures the substitution effects and other price-quantity interactions. The REMI Model also brings into play features of labor and capital markets, as well as trade with other states or countries, including changes in competitiveness. The labor market in the REMI model is linked to a demographic module of population migration. It also includes input substitution between labor and other factors of production, market supply and demand, wage rate determination, and economic geography considerations of labor accessibility of individual industries.The econometric feature of the REMI Model refers to two considerations. The first is that the model is based on inferential statistical estimation of key parameters based on pooled time series and cross-regional (panel) data. This gives the Model an additional capability of being able to extrapolate the future course of the economy, a capability that most other types of economic impact models usually lack.The version of the REMI Model used includes two geographical regions: California and rest of U.S. The model is established based on U.S. and California historical data through 2016. However, in this study we primarily use the California region to simulate the economic impacts of PACE investment in that state.Before undertaking any economic simulations, the estimates of the direct costs and savings of the PACE projects are translated to REMI PI + model inputs. This step involves the selection of appropriate economic activity and policy levers in the Model. 7In the Base Case, we utilize our best estimates of key input variables. For example, these include the average BCRs for earthquake improvements and average insurance savings. They also include our assumption that property owners will repay their financing by displacing their other spending by an equivalent amount. Also, we do not include offset effects of reduced electricity and natural gas demand for the investment displacement in the Base Case analyses. Various adjustments of all of these inputs and assumptions are made in the sensitivity tests below.7.1The results indicate that, during the up-front investment period (2013–2018) of the Ygrene PACE financed property improvements, an average annual increase of GSP of $134.7 million and employment of 1,305 jobs10We also performed decomposition analyses for the GSP and employment impacts to evaluate how the various economic factors affect the aggregate macroeconomic results.Note that the positive impacts of the non-market production of electricity can be added to REMI Model results for Gross Output  to obtain an “Augmented Gross Output” result. Analogous “Augmented GDP” and “Augmented Personal Income” can be estimated by applying value-added or personal income coefficient of the electric utility sector to the “Augmented Gross Output” result. Note, however, that the non-market electricity production does not generate any employment.7.2As our focus is on estimating the macroeconomic co-benefits of PACE financing, we only provide a brief summary of the direct benefits: reductions in water and energy consumption, in GHG emissions, and in losses from natural disasters. It is important to note that these reductions are brought about by financing that basically pays for itself in terms of cost savings on utility bills and avoided costs from the need to repair homes and commercial buildings. The reader is referred to the first partition of Total GDP impacts  are $661.4 million for California. The employment impacts are 9,774 person-year jobs. The largest contributor to these impacts emanate from Energy Cost Savings, followed by the net impacts of Ygrene Financing, Interest Payments and Other Program Fees minus Annual Repayment of this Financing.Total AGDP impacts  are nearly $850 million. The value of Non-Market Electricity Production becomes the third largest contributor to these impacts.7.3In evaluating the contribution of PACE financing to reduce GHG emissions, natural resource use and hazard reduction, it is also important to consider any offsetting effects. Ironically, the same economic stimuli that are the focus of this report have the potential to engender this “offset” effect, in that increases in economic activity generate additional GHGs and utilize more water, natural gas and electricity. The increased amounts emanate from direct effects in the production of inputs into building improvements and the spending of workers who receive income from producing and installing the improvements. They also emanate from the supply-chain effects of production and further consumer spending of additional income generated, as well as consumer spending from energy and water bill savings and reduced costs of natural disasters.11Note that the offset effects for GHGs and electricity are a minimal 12.5% and 2.7%, respectively. However, the offset is a significant 29.4% for natural gas and much more than offsets the direct reductions in the case of water. Note, however, the offset effect represents only 7 ten-thousandths of a percent of baseline water use in the state.Overall, as with regard to most policy improvements, there are some offsetting effects in relation to stated objectives and/or negative side-effects in other domains. Policymakers must weigh these trade-offs in evaluating the policies. In the case of Ygrene financing, the net environmental gains are still positive in three of the four cases. For the remaining case of net increases in water use, it should be noted that those offsets are larger than the savings because of the small percentage of water conservation projects in the state.8We perform several sensitivity tests to analyze how the changes in some key assumptions would affect the macroeconomic impact analysis results of the PACE financing.8.1We first perform a sensitivity test on our assumption that people need to reduce other purchases to repay PACE financing on a dollar-for-dollar basis. It is possible that they will dip into savings, so as not to fully displace other spending. We therefore performed a sensitivity test that reduces the direct offset by 10%. The GDP and employment impacts are increased by 11.4% and 13.0%, respectively, indicating that the Base Case results are not very sensitive to this assumption.8.2We also include the dampening impacts from the decreased demand for electricity and natural gas from the energy supply sectors. These impacts are simulated as decreases in exogenous final demand from the Electricity Generation sector and Oil and Gas Extraction sector in the REMI simulations.The primary reason for excluding this offset in the Base Case is that California law (under Senate Bill 350) requires the state to increase the share of electricity derived from renewable sources from 33% to 50% and to double the efficiency of existing buildings. Additionally, the California Energy Commission (CEC) established the Existing Buildings Energy Efficiency Action Plan to increase energy efficiency in residential, commercial, and government buildings. All of these policies and regulations are entirely independent of PACE, and most of them pre-dated the establishment of PACE programs in these states. Thus, PACE only serves to assist in achieving efficiency and renewable targets that are already mandated by state law and Public Utility Commission regulations. However, it is possible renewable energy and efficiency targets will more than be met by responses to market conditions, in which case PACE displacements of electricity and natural gas would represent an overage and justify including their value in the reduction of electricity and natural gas production. In our sensitivity test, we have thus assumed a 50% offset.Under this offset assumption, the Base Case GDP impacts are reduced from $661.4 million to $482.7 million. Thus, the estimated economic impacts are somewhat sensitive to the inclusion of the offsetting effects of energy demand reduction. This is because over 92% of the Ygrene PACE improvement investment in California is energy efficiency upgrades and renewable energy installations. The esimates for California are most sensitive to this assumption, resulting in a reduction of Base Case GDP Impacts by 27%.8.3A question arises as to the extent to which the various improvements simulated here would have taken place in the absence of Ygrene financing, i.e., whether the impacts are truly additive. Estimates by 99.1PACE financing, like many other government or government-sanctioned programs, has been justified not only by the direct benefits it would generate but also by various co-benefits, one of them being their potential to stimulate the economy. It cannot be taken for granted that positive net economic benefits are forthcoming for every new program, and a careful study of economic impacts, such as that presented above, is warranted. It is then up to policymakers in their program evaluation to determine how much weight to give to these co-benefits. One approach is to juxtapose them along with the direct benefits, of course, to any downsides of the policy/program.PACE financing has not come without criticism. The most significant opposition comes from the Federal Housing Finance Agency (FHFA) concerning the PACE assessment's super lien status, opposition shared by the Mortgage Bankers Association and the National Association of Realtors. PACE financing is voluntarily applied to the property as a special assessment added to the property tax bill, and as such, like all property taxes, is senior to any first mortgage, and thus in the event of foreclosure, only the portion of the PACE assessments that are due at that point in time are paid out before the mortgage. In 2010, the FHFA, as conservator of government sponsored enterprises (GSEs) Fannie Mae and Freddie Mac following the 2008 financial crisis, instituted policy that Fannie and Freddie would not purchase or refinance a mortgage with a PACE assessment. This policy was based on the objection that PACE assessments added risk to Fannie and Freddie's assets by displacing them as prime mortgage holders in the event of property owner default and foreclosure. It is important to note, however, that because PACE is a voluntary special assessment, and cannot be fully accelerated by the taxing jurisdiction, the PACE administrator, or any entity other than the property owner, only the annual PACE assessments in arrears would be due in the event of foreclosure. Thus, the risk to Fannie and Freddie as a result of PACE is not the entire PACE obligation itself, but only the annual assessments past due. Typically, PACE assessments cannot exceed 10–15% of the fair market value of the property, and the combined loan-to-value ratio of PACE and the underlying mortgage cannot exceed 90–100% depending on the state's PACE statute.In December of 2017, the Federal Housing Agency (FHA) issued Mortgagee Letter 2017–18, joining FHFA's opposition to PACE, instituting a policy that it would not insure any new mortgages with a PACE assessment, reversing its previous position put in place in 2016 that it did insure mortgages with PACE assessments. The FHA cited similar reasons as FHFA for its objection to PACE. However, the California Alternative Energy and Advanced Transportation Financing Authority (CAEATFA) established a State-funded loan loss reserve program in 2010 that provided first mortgage holders the ability to recover any losses as a result of residential PACE in the event of foreclosure. Of course, one solution, should default issues accelerate , would b13FHFA and, to a lesser extent, FHA policy opposition toward PACE is one of the most significant barriers to the expansion of residential PACE enabling legislation and broader PACE market expansion. Initial PACE legislation was established in California in 2008. Soon after California, many other states enacted PACE legislation. Today, thirty-six states, as well as Washington DC, have enacted PACE legislation, twenty of which, and Washington DC, have active commercial PACE programs. Yet, only three states – California, Florida, and Missouri – have active residential and commercial PACE programs operating simultaneously.Additionally, there has been some criticism from individuals and consumer protection organizations arguing that PACE financing lacks regulation and needs stricter guard rails to protect consumers from potential abuse from unprofessional contractors and misunderstandings about how PACE works. California has addressed many of these concerns through recent legislationCalifornia's recent legislation, FHFA and FHA policy toward PACE, and broader banking and realtor opposition to PACE have, and will continue to have, significant influence on the future of the national, state, and local PACE markets. As state legislators, federal regulators, and policy makers consider how to approach PACE policy, we intend that our research will play a role in how that policy takes shape. For example, future PACE policy could have a significant impact on whether California will meet its GHG emission reduction goals under Assembly Bill 32 passed in 2006, its goal to double the energy efficiency of existing buildings by 2050 under Senate Bill 350 passed in 2015, and, most recently, its goal to achieve 100% renewable energy by 2045 under Senate Bill 100 passed in 2018.As this research shows, PACE programs can have significant positive impacts on GDP, job creation, energy and water conservation, natural hazard mitigation, and greenhouse gas emission reduction, among others. These positive impacts exceed any negative impacts measured to date.9.2The Property Assessed Clean Energy (PACE) Program has broadened significantly in recent years and now also includes financing for saving water and for reducing vulnerability to disasters for both residential and commercial properties. It is able to achieve its direct societal objectives while providing financial gains to those receiving financing and implementing the building improvements. It does so by providing financing that is beneficial to the recipients by saving money on utility bills and avoiding having to pay for building repairs or reconstruction following an earthquake. Moreover, it represents an equitable alternative collateralization of the financing, in part because the property value is not a criterion for qualifying nor is a credit score, thereby making it available to some who could not otherwise secure financing through more conventional lending-related instruments.We have estimated the direct benefits of PACE financing by one of its major administrators, Ygrene Energy Fund, Inc. More uniquely, we have also estimated the broader macroeconomic co-benefits of this financing in terms of impacts on market-based GDP and augmented GDP that takes into account the non-market effects. The analysis was performed in the context of an Economic Consequence Analysis that took into consideration the numerous positive and negative stimuli associated with PACE financing. The macro impacts were estimated with the use of the REMI Policy Insight Plus Model, the most widely used macroeconometric model at the state and local levels in the US.Total Augmented GDP (AGDP) impacts, which include the non-market value of solar electricity production, social cost of carbon, and avoided disaster relocation cost, are nearly $850 million for California. Employment impacts are the same as the regular employment impacts noted above, since new jobs are not directly associated with any of these direct environmental and hazard reduction benefits.Sensitivity tests indicate our results are robust to changes in major assumptions relating to displacement effects of repaying PACE financing, offsetting effects of energy demand reduction, alternative estimates of insurance savings, and the relative additivity of Ygrene financing. Overall, the PACE financing provided by Ygrene, and even more so if we included all PACE financing, generates sizable net positive impacts on the economies of the major states of operation.co-benefits, which are very positive on net. These co-benefits are thus also likely to be of considerable interest to policy-makers evaluating future decisions about accommodating and even facilitating PACE financing in addition to the broader public policy goals of achieving federal, state, and local energy, environmental, and disaster mitigation goals.PACE is a public policy tool designed to leverage private capital to help homeowners and business owners overcome barriers to implementing building improvements and to provide broader public benefits. PACE financing has successfully reduced air pollution emissions and resource use but has faced criticism by competitors and some analysts because it is not traditional in terms of collateral and because of some marketing and quality control issues of a small percentage of independent contractors that implement the various improvements. The industry addresses this through compliance guidelines for each PACE program. In addition to the more straightforward direct benefits of PACE financing, this paper provides estimates of the several economic PACE is a financing mechanism by which broader and more equitable access to property improvements in energy efficiency, water conservation, renewable energy, and hazard mitigation is made possible. This research shows that PACE has had a positive net impact on the environment and the economy in one of the major states in which it is enabled.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Elective surgeries are primarily scheduled according to surgeon availability with less consideration of patients' postoperative cardiac intensive care unit (CICU) length of stay. Furthermore, the CICU census can exhibit a high rate of variation in which the CICU is operating at over-capacity, resulting in admission delays and cancellations; or under-capacity, resulting in underutilized labor and overhead expenditures.To identify strategies to reduce variation in CICU occupancy levels and avoid late patient surgery cancellation.Monte Carlo simulation study of the daily and weekly CICU census at Boston Children's Hospital Heart Center. Data on all surgical admissions to and discharges from the CICU at Boston Children's Hospital between September 1, 2009 and November 2019 were included to obtain the distribution of length of stay for the simulation study. The available data allows us to model realistic length of stay samples that include short and extended lengths of stay.Annual number of patient surgical cancellations and change in average daily census.We demonstrate that the models of strategic scheduling would result in up to 57% reduction in patient surgical cancellations, increase the historically low Monday census and decrease the historically higher late-mid-week (Wednesday and Thursday) censuses in our center.Use of strategic scheduling may improve surgical capacity and reduce the number of annual cancellations. The reduction of peaks and valleys in the weekly census corresponds to a reduction of underutilization and overutilization of the system. Question: Can the occupancy of a cardiac intensive care unit (CICU) at a high-volume congenital heart center be modeled in a computer simulation to identify optimal elective surgical scheduling practices?Findings: Our CICU weekly occupancy was modeled in a Monte Carlo simulation using historical data as inputs. Scheduling patients with expected hospital stays of 1–2 days earlier in the week on Monday and Tuesday reduced variation in CICU census and the number of annual surgical cancellations.Meaning: Strategic scheduling of surgeries may reduce CICU census variation, increase the number of patients served, and minimize cancellations.Post-operative recovery following congenital heart disease (CHD) surgery requires specialized care in a cardiac intensive care unit (CICU). CICU beds are a scarce resource and require careful planning, allocation, and management –3. In inPostoperative CICU LOS in CHD patients can be accurately predicted pre-operatively through clinical factors and the surgical procedure type that is to be performed . SchedulWe hypothesized that an anticipatory simulation model, where surgeries with short LOS are scheduled earlier in the week and those with long LOS are scheduled later in the week, would reduce the number of surgical cancellations and variation in daily CICU census. By scheduling shorter stay patients on Mondays and Tuesdays, we are hoping to increase discharges later in the week and prevent the naturally occurring higher occupancy rates on Wednesday through Friday. Subsequently, by scheduling longer stay patients Wednesday through Friday, we may decrease weekend discharges and increase utilization of beds when performing elective procedures. The aim of our study was to identify optimal capacity management of our single-center CICU through Monte Carlo simulation of cardiac surgical scheduling. Additionally, we performed a sensitivity analysis to explore the impacts of day of admission, additional OR availability, and CICU LOS on the daily CICU census.We incorporated Boston Children's Hospital Heart Center admission and discharge data over a 10 year period to obtain the empirical distribution of CICU LOS to sample in the simulation study . This CICU LOS distribution and queuing theory principles informed Monte Carlo simulation of 500 years of data (i.e. 500 iterations) in order to model occupancy of the CICU under a variety of settings. We then simulated a 1-year time period of admissions with the same distribution of length of stay to model CICU occupancy rates. Local institutional review board approval was obtained.Boston Children's Hospital Heart Center is a quaternary referral center that provides surgical and non-surgical care to local, national and international patients with congenital and acquired heart disease. The high-volume center is served by a 31-bed CICU and adjoining 46-bed cardiology ward. Elective cardiac surgeries are only scheduled on weekdays. The Heart Center has 3 operating rooms (OR) devoted to cardiac surgery and shares 2 ORs with the orthopedic surgery program. The shared ORs are used weekly by the Heart Center on Tuesdays and Wednesdays. In total, there is capacity to perform 7 cardiac surgical procedures daily on Tuesday and Wednesday, and 5 surgical procedures daily on Monday, Thursday, and Friday.All admissions to and discharge from the Boston Children's Hospital CICU between September 1, 2009 and November 2019 informed the analysis. In total, there were 14,526 admissions. Descriptive data of the informing surgical population is included in recent reports of our program .Medical admissions were neither electively scheduled or predictable. All simulated admissions were controlled through the OR. As medical admissions historically make up 20% of Boston Children's Hospital CICU bed days, for the purposes of anticipatory modeling, we reduced the simulated threshold of our ICU capacity by 20%.The primary outcome of this study was simulated occupancy of the CICU, with simulated anticipated CICU LOS, day of surgery, and number of available OR slots. Between September 1, 2009 and November 30, 2019, there were 14,526 admissions to the CICU. We used the LOS data from this cohort of patients to design the simulation. The LOS data for all patients admitted to the CICU in the study period followed a log-normal distribution . This distribution is well suited for LOS, as it is able to accommodate patients with extended LOS (>30 days) –10. The L is the average number of resources in the queueing system, and in this simulation study is set to the maximum CICU occupancy (25 beds), and W is the mean LOS from historical data (6.56 days)  and the upper bound “b” representing the weekly number of patients who actually arrive, whilst guarding that the number of patients arriving for 52 weeks would not exceed the total annual surgical availability. The minimum number of weekly surgical availability was set to 8, as this reflects the average minimum number of weekly surgical procedures performed at our institution.Monte Carlo simulation, written in the R language (version 3.5.3) , uses th56 days) . The maxIn order to provide recommendations for capacity planning, several scenarios with different simulation parameters were tested to estimate the expected number of cancellations. For each scenario, 500 replications of 365 days were run to simulate a year. At each replication, the throughput of patients was first equilibrated with a 30-day “warm-up” period, which was not included in the total year simulation. The choice of 30 days of warm-up is the typical length of running the event simulation prior to data collection to achieve steady-state performance measures. The warm-up allowed for the unit to achieve a natural level of occupancy without artificially selecting a starting point. Without it, the CICU occupancy would start at 0 patients, which would result in an underestimate of annual cancellations.Next, the daily CICU census was calculated by taking the difference between admitted and discharged number of patients up to each date in a year. The census from the 500 replications of each scenario was aggregated and summary measures, including mean and standard deviation, were captured. The average number of cancellations across replications was calculated by the number of times the daily census exceeded the number of beds in the system.We conducted Monte Carlo simulation experiments that evaluated two models . In the For both models, the optimal arrangement for additional surgical scheduling currently available in the system  was also explored, testing each combination pair of 5 operating days  , A2. To The experiments also varied the levels of occupancy .The density distribution of CICU LOS based on data acquired from the study period and the log-normal density distribution of CICU LOS from Monte Carlo simulation samples are shown in Assessing the average census per weekday , each ofThe anticipatory and random allocation model with optimized allocation of the 2 additional procedures (Models 1B and 2B) decreased the high points on the weekly census and increased the low points; the models showed an increase in the historically low Monday census and a decrease in the historically higher censuses on Wednesday and Thursday . A decreThis study demonstrated the use of a mathematical simulation approach to determine the optimal surgical scheduling parameters for reducing variation in CICU occupancy in a large quaternary pediatric institution. We found that an anticipatory scheduling model may reduce the number of cancellations by 13–14% and result in fewer high occupancy days, but with slightly higher variation in daily CICU census than the currently used model based on patient and surgeon preferences. When the anticipatory model was enhanced by utilizing specific days of the week that optimized additional OR availability, it yielded the least number of annual cancellations and the least variation in daily CICU census among the four scenarios tested.The simulations showed that adopting an anticipatory scheduling approach and changing the allocation of OR availability to have 7 procedures on Wednesday and Friday and 5 procedures on Monday, Tuesday, and Thursday would considerably reduce the number of cancellations and the variation in the CICU daily census. Implementation of this approach is predicated on the ability to predict patient CICU LOS prior to surgical scheduling. We have previously demonstrated that pre-operative characteristics of this patient population can be used to predict post-operative CICU LOS with operationally adequate accuracy, and have incorporated these models into local capacity predictions , 6. In sBy understanding patterns of CICU occupancy, clinicians and administrators can identify and shift care models to efficiently manage available CICU beds. In general, beds are managed by crude historical estimates of need. This simulation of CICU census can help institutions make more informed decisions regarding optimal scheduling which will in turn, improve quality of care and reduce waste. High ICU occupancy rates have been shown to be associated with increased mortality, adverse events, and cancellations, while low occupancy leads to unnecessary expenses , 14.This simulation approach can also aid in long-term planning of CICU operations, such as calculating staffing needs by using a projected daily census. Our Heart Center will be expanding into a new building in 2022 to 6 ORs, 48 CICU beds, and 48 inpatient rooms in the step-down unit. Modeling the new CICU with the building's parameters can inform staffing needs and help develop strategies to ramp up operations without producing excessive waste, delaying patient procedures, or impacting quality of care.Furthermore, this simulation study could be readily tailored to other institutions and ICUs. It incorporates data from the daily ICU census and the parameters of the design could be targeted to unique institutional structural or programmatic requirements. Another advantage of our approach is that it considers the downstream capacity limitations brought on by ICU occupancy. Other simulation studies do not always consider the link between the throughput of surgeries and capacity of the ICU. They model the number of ORs needed or the ICU occupancy independently of the center's surgical practices , 15. FurOur study presents several limitations. First, it does not account for developments in treatments, clinical practices, or changes in cardiac surgeon staffing over the 10-year period of retrospective data that may impact recovery time, complications, or typical lengths of stay. In the simulation, however, the likelihood that an admission was sampled did not depend on the date of the admission. At our institution, there have not been any systematic scheduling procedures; surgeries have always been scheduled according to patient acuity, surgeon preferences and OR availability. Thus, we did not believe that changes in treatments or practices would significantly impact the accuracy of the model. Further, our results are generalizable to other centers with a patient cohort that has a distribution of length of stay that is similar to that used in this study; otherwise, the recommended scheduling may differ from that presented here. Lastly, the historical data did not exclude non-surgical patients, so the model sampled patients who underwent surgery in the OR and patients who did not. The medical patients only account for 10–15% of admissions to the CICU and the CICU LOS distribution of surgical patients has a wide range, so including the medical patients likely did not significantly skew the data. In future studies, the patients can be distinguished by medical and surgical admission to further explore CICU occupancy patterns.We developed a Monte Carlo simulation of CICU occupancy that models the weekly and daily censuses of the CICU from historical data. The results of the simulation experiments can help identify strategies to reduce the number of surgical cancellations and optimize ICU capacity at a high-volume center. By scheduling patients with an expected short stay to be early in the week and patients with an expected long stay to be later in the week, the variation in weekly CICU census decreased, thus reducing heterogeneity in weekly occupancy. Reducing CICU census variation also resulted in an increase in the annual number of patients served, while minimizing cancellations under the same system constraints. Similar simulation models can be created and applied to non-cardiac ICU and other types of units in order to optimize resource use and improve efficiency and cost-effectiveness of care.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.Local institutional review board approval was obtained. Written informed consent from the participants' legal guardian/next of kin was not required to participate in this study in accordance with the national legislation and the institutional requirements.DM, PA, and LB: conceived the research idea. EE: designed the simulation and undertook the analyses and interpretation with the supporting supervision and feedback from DM and LS. EE and SG: drafted the paper and revised it following comments from all of the authors. All authors contributed to the interpretation of the results and critically revised the manuscript. All authors approved the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Developing ecotourism services is a suitable solution to help developing countries improve the status of sustainable development indicators and protect their environment. The primary purpose of this paper is to find out the effects of green governance variables and carbon dioxide emissions on ecotourism for 40 developing economies from 2010 to 2021. The results confirmed a uni-directional causal relationship between the green governance indicator and the inflation rate of the ecotourism indicator. In addition, with a 1% improvement in the green governance index of developing countries, the ecotourism of these countries will increase by 0.43%. In comparison, with a 1% increase in the globalization index of these countries, ecotourism will increase by 0.32%. Moreover, ecotourism in developing countries is more sensitive to macroeconomic variables changes than in developed economies. Geopolitical risk is an influential factor in the developing process of ecotourism. The practical policies recommended by this research are developing the green financing market, establishing virtual tourism, granting green loans to small and medium enterprises, and government incentives to motivate active businesses. The challenge of climate change has become a primary threat to living on the Earth in the last centuries , and increasing welfare in the country hosting the tours. According to the Yearbook of Tourism Statistics published by the World Tourism Organization, international tourism has increased from 522.2 billion US dollars in 1995 to nearly 1.86 trillion US dollars in 2019. This increase shows the importance of tourism services in generating income for countries, especially in the era of Corona and post-corona. Casado-Aranda et al.  express Ecotourism is an approach based on environmental criteria, which is opposed to over-tourism . The International Ecotourism Society defines Ecotourism as an efficient way to conserve the environment and improve local people’s well-being. It can be said that Ecotourism, along with various economic advantages , will bring environmental protection to the world’s countries, achieving the goals of green economic growth recovery and sustainable development. Xu et al.  data.Selecting a sample of 40 developing countries from different geographical regions to calculate the interconnections between ecotourism, green governance, and climate changeMaking a further discussion to address the role of uncertainty and the developing level of countries in the relationship between ecotourism and explanatory variables.This paper tries to contribute to the existing literature from the following aspects:The main results confirm the existence of a uni-directional causal relationship running from the green governance indicator and inflation rate to the ecotourism indicator. In addition, with a 1% improvement in the green governance index of developing countries, the ecotourism of these countries will increase by 0.43%. A 1% increase in the globalization index of these countries accelerates ecotourism by 0.32%.Moreover, ecotourism in developing countries is more sensitive to macroeconomic variables changes than in developed economies. Geopolitical risk is an influential factor in the developing process of ecotourism. The practical policies recommended by this research are developing the green financing market, establishing virtual tourism, granting green loans to small and medium enterprises, and government incentives to motivate active businesses.The paper in continue is organized as follows: section “Literature review” provides a short literature review to determine the gaps this research seeks to fill. Section “Data and model specification” argues data and model specification. The following section represents empirical results. Section “Discussion” expresses discussion, whereas the last section provides conclusions, policy implications, research limitations, and recommendations to research further.This part of the article analyzes and classifies the previous literature on ecotourism and sustainable development in a rational and structured way. The importance of tourism in economic growth and development has been discussed in previous studies. However, the study of the effect of tourism on climate change has received little attention. Especially the relationship between sustainable tourism, climate change, and environmental policies is a problem that has yet to receive the attention of academic experts.A group of previous studies has focused on the place of tourism in economic development and growth. Holzner  focused Another group of previous studies has linked tourism to sustainability targets. Sorensen and Grindsted  expresseTourism that can pay attention to environmental issues is called “ecotourism.” Many new studies have studied different dimensions of ecotourism. Lu et al.  expandedSeveral earlier studies have concentrated on the characteristics of ecotourism in different developed and developing economies. For example, Ruhanen  investigAnother group of earlier studies has drawn attention to promoting eco-tourism in the post COVID era. They believe that the corona disease has created an excellent opportunity to pay more attention to environmental issues and that countries should move towards sustainable development concepts such as sustainable (eco) tourism in the post-corona era. Soliku et al.  studied A review of previous studies shows that tourism can positively impact green growth and sustainable development. Sustainable tourism can be used as a policy to deal with the threat of climate change. This issue needs more attention in the corona and post-corona eras. Because in the post-corona era, many countries have sought to revive green economic growth, and ecotourism can be one of the tools to achieve it. As observed, a detailed study of the relationship between climate change, ecotourism, and environmental policies has yet to be done. Therefore, this research will address and fill this literature gap.The paper seeks to find the relationship between climate change, ecotourism, and environmental policy for the panel of 40 developing economies from different regions from 2010 to 2021 (480 observations). The sample size could have been more extensive due to the lack of information on some variables. However, there are 480 observations in the data analysis of the data panel; therefore, the number of samples selected is acceptable.To determine the proxies for main variables, CO2 emissions per capita are selected as the proxy for climate change. Many earlier studies  data from World Bank are gathered to calculate this variable. With the improvement of the Green Governance Index, the quality of environmental policies will also increase, and vice versa. With the adverseness of the Green Governance Index, the efficiency of environmental policies will decrease.Regarding control variables, the inflation rate as an influential factor in tourism flows is selected. The importance of this variable to promoting/declining tourism flows has been drawn to attention by some earlier studies, such as Liu et al. . The infi is 1,2,…,40 and shows examined developing economies, while t is time and contains 2010, 2011,..,2021.According to the variables mentioned above, 40 examined developing countries from 2010 to 2021, the panel co-integration model can be written as Eq. Prior to the estimation of coefficients of Eq. θ and ECT represent the fixed country effect and error correction term.In the above Equations, Δ is the first differences of variables, while The next step is the long-run panel co-integration estimations. To this end, Fully Modified OLS (FMOLS) and Dynamic OLS (DOLS) as robustness checks are conducted, which are two famous panel co-integration estimators , Norway (0.961), Iceland (0.959), Hong Kong (0.952), Australia (0.951), Denmark (0.948), Sweden 0.947) and Ireland (0.945)). The selected variables, explained in section “Data and model specification”, are collected from 2010 to 2021. The panel unit root tests confirmed that all series are non-stationary at the level and become stationary after a first difference. In addition, the presence of co-integration linkages among variables is revealed by the panel co-integration test. The panel co-integration estimator of FMOLS is employed to study the long-term relationship among variables. The findings are reported in Table  and IrelAccording to the estimated coefficients, the green governance indicator positively and statistically significantly impacts ecotourism in the examined developed economies. However, the magnitude of the impact of this variable is more considerable for developing countries because these countries have more imbalances in markets and regulations. Therefore, the presence of good green tourism can have a more positive effect on advancing the goal of ecotourism. Contrary to the findings of developing countries, carbon dioxide emission in developed countries has a negative and significant effect, meaning that with an increase of 1% in carbon dioxide in developed countries, the level of ecotourism becomes more unfavorable by 0.034%. Moreover, inflation and globalization variables have significant negative and positive coefficients, respectively. However, the magnitudes of these two variables’ coefficients are also higher in developing countries. Ecotourism in developing countries is more sensitive to changes in macroeconomic variables such as green governance, globalization, and inflation.Another difference between eco-tourism in developed and developing economies may be interpreted through the term “greenwashing,” introduced by Westerveld in 1986  can motivate small and medium-sized companies in the field of activities related to ecotourism. Government incentives to motivate businesses active in ecotourism and government deterrent policies (green tax) from businesses active in the field of tourism to lead them to increase the share of ecotourism in their activities can be a proper operational strategy. In developing countries, the role of government and green governance is vital in advancing the goals of ecotourism. By improving the level of its green governance, the government can create efficient policies, regulations, and social tools to create motivation and desire to accept ecotourism, an essential and undeniable issue in developing societies. Creating a guarantee fund for ecotourism companies in developing countries is another practical policy to support these companies financially. Guarantee funds can be established with the participation of the people of ecotourism destinations in order to strengthen the financial strength of ecotourism companies in these destinations.In order to achieve the promotion of ecotourism in developing countries, the implementation of integrated and effective strategic and practical policies is of great importance. According to the concluding remarks mentioned, practical policies are presented as follows for enhancing ecotourism in developed countries. The development of ecotourism requires the improvement of various infrastructures and mechanisms, which depends on the implementation of projects related to ecotourism in developing countries. Because most countries do not have enough financial power to invest in such projects, developing the This research had a practical and innovative contribution to the literature on ecotourism in developing countries. The findings obtained from the econometric model analysis provided appropriate practical and strategic policies to the policymakers of countries interested in the development of ecotourism. However, access to data related to the ecotourism index and sustainable development of developing countries due to the lack of community in a specific database is considered one of the critical limitations of this research. This limitation caused many developing countries to be excluded from the research sample, which may have created a deviation in the research. Adding more countries to the test sample in future research is suggested to obtain complete and accurate results. Also, due to the outbreak of the Corona pandemic at the end of 2019 and the Russia-Ukraine war since the beginning of 2022, it is suggested that these two variables be included in the econometric model as an illusion in order to analyze their effects on the ecotourism of the countries of the world. Using other econometric methods, such as artificial neural networks, is suggested to model ecotourism in different countries. Complex modeling by taking into account trends and trends to predict the relationship between variables in the future will be an essential step in formulating effective programs in ecotourism.