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Environmental Impacts of Green Open Space in Urban Indonesia: A Difference-in-Differences Analysis

ILMIAWAN AUWALIN

https://orcid.org/0000-0003-1520-1190

Department of Economics, Faculty of Economics and Business, Universitas Airlangga, Surabaya, Indonesia

E-mail Address: auwalin@feb.unair.ac.id

Corresponding author.

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RUMAYYA

https://orcid.org/0000-0001-8216-3628

Department of Economics, Faculty of Economics and Business, Universitas Airlangga, Surabaya, Indonesia

E-mail Address: rumayya@feb.unair.ac.id

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, and

NI MADE SUKARTINI

https://orcid.org/0000-0001-8021-0689

Department of Economics, Faculty of Economics and Business, Universitas Airlangga, Surabaya, Indonesia

E-mail Address: ni-made-s@feb.unair.ac.id

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https://doi.org/10.1142/S0116110525500039Cited by:0 (Source: Crossref)

Abstract

This study investigates the impact of green open spaces in reducing the probability of flooding and open waste burning in urban areas in Indonesia’s three largest metropolitan cities: Surabaya, Jakarta, and Medan. This study employs urban village microdata from the 2014 and 2018 Village Potential Census. First, we construct the dataset into a difference-in-differences setup. The urban villages that initially did not have any green open spaces in 2014 and then had them in 2018 were assigned as the treatment group, and those without any green open spaces in both periods were the comparison group. Then, we estimated the impact of urban green spaces on the probability of flooding and open waste burning. The results indicate that the likelihood of flooding and open waste burning had decreased in treated areas by 2018.

This research is funded by the Penelitian Dasar Unggulan Perguruan Tinggi 2019 grant from the Indonesian Ministry of Higher Education.

Keywords:

JEL: R11, R52

I. Introduction

As the development of urban areas accelerates in developing countries, environmental problems are also increasing. Flooding is among the most common environmental issues and has become an increasingly serious concern for cities in developing countries. Floods in urban areas usually occur due to urbanization, including large-scale infrastructure development (Kim, Lee, and Sung 2016; Nur and Shrestha 2017). Rapidly growing urban settlements in developing countries are and will continue to be vulnerable to flooding. Populations and assets in flood-prone locations in the world’s urban areas are growing. Between 2003 and 2018, more than 11,000 cities in more than 200 countries suffered from large-scale flooding (Kocornik-Mina et al. 2020, Gandhi et al. 2022). The issue of flooding is being exacerbated by climate change, which alters global climate patterns and the intensity of flooding (Kleinen and Petschel-Held 2007, Douglas et al. 2008, Mirza 2011). The occurrence of flooding causes losses, forcing cities in developing countries to urgently address this serious problem.

Air pollution is another common environmental issue in urban settings in developing countries. In addition to air pollution from the manufacturing and transportation sectors, another primary cause of air pollution in developing countries is the smoke caused by open burning. Air pollution from such burning occurs not only in rural areas where agricultural waste is burned openly (Andini et al. 2018, Junpen et al. 2018), but also in urban areas where household solid waste is burned openly (Pansuk, Junpen, and Garivait 2018; Okedere et al. 2019; Krecl et al. 2020). With the high population density of urban areas, air pollution directly impacts public health. The World Health Organization estimated that increased urban air pollution in developing countries has caused more than 2 million deaths per year as well as various respiratory ailments (Cities Alliance 2007, World Health Organization 2014).

Flooding and air pollution are also common problems in Indonesia. Figure 1 shows the consistency of a high number of cases of flooding in Indonesia from 2010 to 2019. Although flooding decreased relatively significantly from 2010 to 2011, the trend of flooding began rising again in 2015, reaching 1,276 recorded floods in 2019 (National Agency for Disaster Management 2021).

In addition to the issue of flooding, the problem of Indonesia’s poor air quality—as evidenced by its high level of carbon dioxide emissions—is another concern. Indonesia’s annual carbon dioxide emissions per capita has exceeded 1.7 metric tons since 2010, reaching 2.25 metric tons per capita in 2019.¹ The high level of carbon dioxide emissions has been linked to chronic respiratory diseases that are affecting the Indonesian population with greater frequency (Kunii et al. 2002, Duki et al. 2003). Therefore, flooding and air pollution are both critical challenges in Indonesia, particularly in major cities such as Jakarta, Medan, and Surabaya that have large populations and are located along a shoreline, making them more prone to flooding.

Due to the considerable adverse effects of flooding and air pollution on the general welfare of humans and the environment in urban areas, city governments have implemented various preventative measures to minimize these negative effects. One of the ameliorative measures being pursued is the establishment of urban green open spaces. Several recent studies have identified the role of urban green open spaces in reducing flooding in urban areas (Liu et al. 2013; Zhang, Li, and Wang 2015). Other studies also show that the reduction of green open space increases the frequency of flooding (Abass et al. 2020). Regarding air pollution, several studies have identified the critical role of urban green open spaces in reducing carbon dioxide levels and improving air quality (Chen et al. 2015, Ren et al. 2017). This study extends the literature by investigating the impact of urban green open spaces in reducing urban environmental issues, particularly flooding and air pollution (due to open waste burning in urban areas), in Indonesia.

This study focuses on three metropolitan cities in Indonesia: Jakarta, Medan, and Surabaya. The unit of analysis in this study is the urban village. Urban villages in Indonesia are also known as kelurahan, which refers to the country’s lowest governmental administrative level. Urban villages are like rural villages (desa) except that they are part of cities (kota), not districts (kabupaten). Cities and districts have the same government administrative structure, but they are differentiated by population size and economic activity. Districts are usually larger in physical size and therefore include more subdistricts (kecamatan). Each subdistrict consists of villages and urban villages. In Indonesia, a region is classified as a city if it has a population of more than 500,000 people. The three cities included in this study are densely populated and comprise a total of more than 540 urban villages. Jakarta, with a population of over 11 million and covering 664 square kilometers (km²), has a population density of more than 16,500 people per km². Surabaya, with a population of almost 3 million and an area equal to 323 km², has a population density of almost 9,300 people per km². Finally, Medan, with a population of almost 2.5 million and a size of 265 km², has more than 9,400 people per km².

The difference-in-differences (DID) estimation model is employed in this study to establish causal relationships between urban green open spaces and the probability of flooding and open waste burning occurring in Indonesia’s urban areas. In the DID setup, urban villages in the 2014 and 2018 Village Potential Census (PODES) from three major metropolitan cities in Indonesia—Jakarta, Medan, and Surabaya—are divided into the treatment group and the comparison group. Using the two periods of the 2014 and 2018 PODES, the urban villages that initially did not have any green open spaces in 2014 and then had them in 2018 are assigned as the treatment group. On the other hand, the urban villages with no green open spaces in both periods are set as the comparison group. The estimation results show that the presence of green open spaces in urban areas reduces the likelihood of flooding by 30.7% and the probability of open waste burning by 10.6%.

The remainder of this paper proceeds as follows. Section II reviews the literature on the role of green open spaces in reducing urban environmental problems, particularly flooding and open waste burning. Section III reviews the context of the study, which considers the case of Indonesia. Section IV presents the data and research methods. Section V discusses the results and analysis. Finally, section VI provides a summary and our conclusions.

II. The Role of Green Open Spaces in Reducing Urban Environmental Problems

A. The Role of Green Open Spaces in Addressing Flooding

Flooding is a natural phenomenon that appears to positively impact ecosystems (Mirza et al. 2005). Unfortunately, human activities that degrade ecosystems by decreasing the soil’s capacity to absorb excess water and prevent excess flooding can turn such flooding into catastrophic events (Bravo de Guenni et al. 2005; Vorosmarty, Leveque, and Revenga 2005). As climate change progresses, dense populations in growing urban areas are becoming more vulnerable to flooding. Between 2003 and 2008, large-scale flooding occurred in 1,868 cities in 40 countries (Kocornik-Mina et al. 2020). While between 2012 and 2018, around 9,468 cities in 175 countries suffered from large-scale flooding (Gandhi et al. 2022). The study by Kocornik-Mina et al. (2020) finds that urban economic activity is more likely to be concentrated in low-lying areas that are susceptible to flooding. Utilizing annual data on nighttime lights, they find that significant floods cause a 2%–8% decrease in nighttime light intensity in the year following a major flood. In the year immediately after a flood event, however, economic activity returned to their preflood levels. They also find that, with the exception of recently settled parts of cities, economic activity does not move to safer locations following floods.

A more recent study by Gandhi et al. (2022) finds that cities categorized as “risky” due to past severe flooding experiences have slower urban population growth rates, which is particularly evident in high-income countries, indicating a decline in an area’s desirability for potential residents. However, the effect of flooding in reducing population growth is modest at around 0.4–0.5 percentage points. Using nighttime lights data, they also reveal that floods significantly diminish economic activity, especially in low-income countries, with high-altitude cities in such countries suffering the most. Recovery to predisaster economic levels is quicker in high-income countries compared to low-income ones. The study also indicates some adaptation and resilience to climate shocks, with wealthier cities experiencing less severe economic impacts from floods, as flood protection infrastructure, such as dams, aid in mitigating the negative effects of floods on economic activity—though population growth patterns vary depending on the risk level and presence of dams. These findings collectively underscore the importance of adaptive measures in urban planning and disaster management to reduce the adverse effects of floods on both population dynamics and economic activity.

Considering the large costs for cities associated with flooding, various studies try to identify ways to minimize flooding in urban areas. In this regard, water-sensitive urban planning infrastructure—such as rain storage tanks, constructed wetlands, bio-retention swales and basins, and stormwater storage in green spaces—have been identified as effective measures to reduce the volume of rainwater flow that has the potential to trigger flooding (Barton and Argue 2007; Coombes 2009; Walsh, Fletcher, and Burns 2012). This water-sensitive urban planning infrastructure is intended to integrate urban water systems with natural water systems in the hydrological cycle (Barton and Argue 2007). Urban green spaces increase the retention of hydrological systems in urban areas and reduce the amount of water flow that causes flooding (Bai et al. 2018). In addition, green spaces can also increase the water absorption rate of the soil and provide greater water storage capacity in cities. Therefore, the preservation, restoration, and expansion of urban green spaces is expected to be a potentially effective option in reducing the risk of flooding in urban areas. Unfortunately, urban infrastructure in developing countries is often outdated and unable to cope with the increase in precipitation occurring in some areas due to climate change. Meanwhile, the consequences of flooding often cause losses of life and property. Therefore, it is essential to consider green urban designs to minimize flooding in urban areas.

B. The Role of Green Open Spaces in Addressing Open Waste Burning

How waste is handled affects everyone and everything—from individuals and small businesses to public authorities and international trade (Sivertsen 2006). An increase in the amount of waste generated occasionally indicates inefficiencies in using resources such as energy and materials. Open burning of municipal waste, which is commonly practiced in developing countries, releases harmful pollutants into the air, including delicate particulate matter and black carbon, and worsens water quality in urban areas (Krecl et al. 2020). A recent bibliometric by Ramadan et al. (2022) on open waste burning between 2007 and 2021 shows the scale of open waste burning in several countries. They find that open waste burning in India contributed to about 20% of the country’s air pollution. In Thailand, more than 50% of domestic waste is burned, while in Kano, Nigeria, about 66% of waste is burned openly, often in private backyards. These figures should raise concerns among policymakers and researchers on the environmental issues and health risks arising from open waste burning practices.

When open burning happens in urban areas, the impact on people and the environment is more severe (Kumari et al. 2019). With urban areas’ high population density and limited amounts of greenery to absorb air pollution, open burning has harmful direct impacts. Green open spaces can help minimize these negative consequences as green space improves air quality by absorbing sulfur oxides, carbon monoxide, and nitrogen oxides (Miller 1997). A survey of plants’ ecological function in city parks finds that the urban greeneries can filter up to 85% of the air pollution around them (Bolund and Hunhammar 1999). In addition, an evaluation of the environmental benefits of urban trees in southern regions of the People’s Republic of China indicates that an increase of trees in urban areas can have massive and permanent effects on reducing air pollution such as carbon dioxide, sulfur dioxide, and especially total suspended particulates (Jim and Chen 2008).

Furthermore, we maintain that the increased prevalence of green open spaces can also reduce the occurrence of open waste burning in an urban setting. In Indonesia, the establishment of green open spaces has often been the result of the enactment of environmental regulations, as was the case with the significant expansion of green open spaces in Surabaya since 2000 (Hasyimi and Abi Suroso 2017). In 2000, the city government issued an environmental regulation allocating and establishing green open spaces and managing waste disposal through the banning of nonenvironmentally friendly activities such as solid open waste burning.

The availability of proper waste management with regular waste collection is one of the key factors that minimize open waste burning in urban areas (Nagpure, Ramaswami, and Russell 2015; Reyna-Bensusan, Wilson, and Smith 2018). A study in Indonesia by Putri (2020) finds that provinces with environmental regulations requiring a higher proportion of environmental facilities, including proper waste management, tend to have fewer cases of open burning done by households than provinces with smaller environmental budget allocations. A study in the Philippines by Saplala-Yaptenco (2015) shows the importance of central government laws and local government ordinances in prohibiting the open burning of solid waste. Another study in the United States found that the lack of federal regulation of backyard burning resulted in problems with the backyard burning of domestic waste in rural areas (Lighthall and Kopecky 2000). However, a study in Indian cities by Ramaswami, Baidwan, and Nagpure (2016) found that simply implementing a legal ban on solid waste burning was insufficient to reduce open waste burning; the ban needs to be supported by the provision of infrastructure for waste pickup. In addition, the study also finds that informal restrictions from residents and neighborhood associations can play a significant role in restricting solid waste burning at the neighborhood level. Thus, based on this study, a legal ban needs to work in conjunction with improved waste management, as well as peer pressure from neighbors, to effectively reduce open waste burning.

III. The Indonesian Context

A. Government Policies on Green Open Spaces

The urban green open space, in general, is an area or lane within a city in which usage is open to the public. The green open space is part of a city’s open space, usually for humans and other creatures to sustain and develop. It is called a “green area” because it is a place for plants to grow, whether they grow naturally or are planted, to give a green and shady impression. Examples of green areas include city parks, green lines along roads and rivers, green corridors, urban forests, roof and vertical greening, and private gardens and domestic gardens (Cameron et al. 2012). According to Pradipta (2020), the primary function of green open spaces is to help balance the ecological conditions of the city. Trees and plants help absorb carbon dioxide and store water. Apart from environmental benefits, other benefits of green open spaces include facilitating social interaction and providing environmental education—by offering hands-on learning experiences, promoting biodiversity observation, and fostering a sense of stewardship and sustainability through practical engagement with nature. Furthermore, green open spaces can be used economically as natural tourist spot (ecotourism) in urban areas. Other benefits of green open spaces are providing comfort and environmental aesthetics for urban areas that are otherwise mainly only rows of buildings.

To promote green cities, the Government of Indonesia introduced the Adipura program in 1986. This program was designed to reward district governments in Indonesia for keeping the region organized and clean. The Adipura program was complemented by a program for promoting green open spaces through the introduction of the Spatial Planning Act, 2007 (Prihandono 2010). Under this act, the ideal proportion of cities’ green open space is determined to be 30%, comprising 20% public green open space and 10% private open space. Unfortunately, Indonesian cities fall short of the size requirement for green open spaces. For instance, Jakarta, the capital of Indonesia, with an area of 664km² should have at least 200km² of green open space. However, Jakarta currently only has approximately 33km² of green open space, representing only 5% of its total area. Urban land uses for infrastructure, including buildings and contemporary shopping malls, have prevented the 30% goal from being reached. Due to rapid infrastructure growth in Indonesia over the past 30 years, the country’s green spaces have diminished nationwide. In 2019, only 13 out of 174 Indonesian cities had green open spaces specifically included in their development plans, according to the most recent data from the Ministry of Public Works and Public Housing.

The Spatial Planning Act, 2007 is supported by ministerial regulations such as the Regulation of the Minister of Home Affairs Number 1 of 2007 on the Planning for Urban Green Open Space. This regulation states that urban green open spaces are part of metropolitan areas, with plants providing ecological, social, cultural, economic, and aesthetic benefits. The Regulation of the Minister of Public Works Number 5 of 2008 on the Guidelines for the Provision and Utilization of Green Open Spaces in Urban Areas defines green open spaces’ purpose as maintaining the balance of urban environmental ecosystems to improve the quality of urban living. This regulation also states the five functions of urban green open spaces: (i) securing the existence of urban protected areas; (ii) controlling pollution and damage to soil, water, and air; (iii) protecting biodiversity; (iv) managing the water system; and (v) providing urban aesthetic facilities. The regulation also emphasizes the benefits of urban green open spaces in reflecting regional identity, providing research and education facilities, offering active and passive recreational facilities and social interactions, increasing the economic value of urban land, and fostering a sense of pride and increasing regional prestige.

The combination of the Adipura program and the laws and regulations related to green cities is expected to encourage city and district governments in Indonesia to prioritize green open spaces and synchronize urban development processes. While the Adipura program focuses on waste management and cleanliness, the green city law pushes city governments to establish at least 30% of their land area as green open space. According to Dethier (2017), even though law enforcement is relatively weak in Indonesia, both the Adipura program and the green city law and regulations have been relatively successful in transforming many cities in Indonesia. Dethier (2017) argued that this success is probably related to the effective reputational incentives of green open space programs. Culturally, gaining honor and avoiding shame have helped to improve municipal performance with regard to green open spaces.

B. Urban Growth and Urban Environmental Issues in Indonesia

The urban growth rate in Indonesia is relatively high, with an estimated growth of 2% per year, which is on par with India and one-third of the urban growth rate of the People’s Republic of China (Olivia et al. 2018). Indonesia’s urban growth is mostly driven by the growth of the overall population. Population growth in urban areas is associated with greater demand for food and social infrastructure, and increased employment opportunities and poverty. A study by Gibson, Jiang, and Susantono (2023) using satellite observations of nighttime lights to measure urban growth in Indonesia finds that the growth of lighted areas in major cities is positively associated with poverty, while the expansion of lighted areas in secondary towns is negatively associated with poverty. Based on this finding, reducing poverty through the expanded development of secondary towns might be an option for policymakers. However, as larger cities with larger populations drive more economic activity, output, and productivity (Peng, Chen, and Cheng 2010), policymakers may tend to promote the growth of big cities. In this regard, promoting big cities with large urban populations would lead to increased demand for food and other economic goods in urban areas. This high demand must be met by increased agricultural productivity, industry, and services. Unfortunately, the combination of high demand and high production activities is associated with larger urban domestic waste, pollution, and environmental degradation. This relationship has been identified in the case of urban growth in Indonesia (Drescher and Laquinta 2002, Muggah 2012, Glaeser 2013, Dethier 2017). Therefore, mitigating the negative impact of urban growth is crucial. In the case of Indonesia, Lewis (2014) argues that the law and enforcement of environment protection, such as waste management and green open space promotion, can help mitigate the negative impact of urban growth.

IV. Data and Methodology

To examine the role of green open spaces in reducing environmental issues in urban villages, we use the PODES datasets from 2014 and 2018. The datasets contain micro-level statistics for Indonesian rural and urban communities gathered by Statistics Indonesia. This study covers three major metropolitan cities in Indonesia—Jakarta, Medan, and Surabaya—consisting of more than 540 urban villages (Figure 2). With a population of more than 11 million, Jakarta is Indonesia’s largest city and its capital. With a population of close to 3 million, Surabaya ranks second in size, while Medan, the largest metropolitan city on the island of Sumatra, has a population of 2.4 million.

Figure 2. **The Locations of Jakarta, Medan, and Surabaya**
Source: Authors’ illustration.

In this study, we use the DID approach, a quasi-experimental method used in econometrics and quantitative social science research, to determine the different effects of treatment on a treatment group compared to a comparison group using data from observational studies. To identify the impact of a treatment (from the explanatory variable or independent variable) on an outcome (i.e., the dependent variable or outcome variable), this method compares the average difference in the outcome variable for the treatment group with the average difference over time for the comparison group. To prevent selection bias, we need to eliminate the inessential influences (Angrist and Pischke 2008).

DID is mainly used in this research to explore the impact of the presence of green open space on two urban environmental issues: flooding and open waste burning. DID exploits variations in the presence of green open space in urban villages in the three major Indonesian metropolitan cities included in this study: Jakarta, Medan, and Surabaya. It compares the outcome of the treatment group (urban villages with green open space in the second period) and the comparison group (urban villages without green open space in both periods). The treatment group is coded as 1, while the comparison group is coded as 0. The interaction term in the DID estimation model indicates the effect of the presence of green open space on the urban environmentally related issues by comparing the two groups’ differences. The variable is constructed by multiplying the two variables. The model is constructed as follows :

y i g t = A g + B t + β D g t + ε i g t, y_{i g t} = A_{g} + B_{t} + {β D}_{g t} + ε_{i g t}, <math display="block" altimg="eq-00004.gif"><msub><mrow><mi>y</mi></mrow><mrow><mi>i</mi><mi>g</mi><mi>t</mi></mrow></msub><mo>=</mo><msub><mrow><mi>A</mi></mrow><mrow><mi>g</mi></mrow></msub><mo>+</mo><msub><mrow><mi>B</mi></mrow><mrow><mi>t</mi></mrow></msub><mo>+</mo><msub><mrow><mi>β</mi><mi>D</mi></mrow><mrow><mi>g</mi><mi>t</mi></mrow></msub><mo>+</mo><msub><mrow><mi>ε</mi></mrow><mrow><mi>i</mi><mi>g</mi><mi>t</mi></mrow></msub><mo>,</mo></math> (1)

where

A_{g}

$A_{g}$ is the fixed effect of the treatment or comparison group,

B_{t}

$B_{t}$ is the fixed effect of the before or after time periods,

D_{g t}

$D_{g t}$ is a dummy variable equal to 1 for the treatment group and 0 otherwise, and

ε_{i g t}

$ε_{i g t}$ is the error term. The DID estimate is represented by the beta coefficient. We estimate the model using ordinary least squares; for the robustness check, we also estimate the model using probit.

We set the fixed effect in the DID estimation to adjust the time-invariant factor, such as the location characteristics of the urban villages, using the panel structure of the data. We use the DID estimator to handle the unobservable differences that do not change over time between the control and treatment groups in the initial period (Meyer 1995, Wooldridge 2002, Angrist and Pischke 2008, Gertler et al. 2011).

Based on the presence of green open space in the first period (2014) and the second period (2018), we determine the control and treatment groups. If an urban village reported the nonexistence of green open space in 2014 but had at least one in 2018, the urban village is assigned to the treatment group. Meanwhile, an urban village that reported no green space in both 2014 and 2018 is assigned to the comparison group. According to Wooldridge (2002), the DID estimation is a simple method to eliminate the variations that could result from factors that have an impact on both the control and treatment groups. Given that we are utilizing a panel dataset, the econometric model specification is as follows :

E n v i t = β 0 + β 1 u r b v i l i + p o s t t + δ i u r b v i l i \times p o s t t + δ X i t + ε i, {E n v}_{i t} = β_{0} + β_{1} {u r b v i l}_{i} + {p o s t}_{t} + δ_{i} {u r b v i l}_{i} \times {p o s t}_{t} + {δ X}_{i t} + ε_{i}, <math display="block" altimg="eq-00009.gif"><msub><mrow><mi>E</mi><mi>n</mi><mi>v</mi></mrow><mrow><mi>i</mi><mi>t</mi></mrow></msub><mo>=</mo><msub><mrow><mi>β</mi></mrow><mrow><mn>0</mn></mrow></msub><mo>+</mo><msub><mrow><mi>β</mi></mrow><mrow><mn>1</mn></mrow></msub><msub><mrow><mi>u</mi><mi>r</mi><mi>b</mi><mi>v</mi><mi>i</mi><mi>l</mi></mrow><mrow><mi>i</mi></mrow></msub><mo>+</mo><msub><mrow><mi>p</mi><mi>o</mi><mi>s</mi><mi>t</mi></mrow><mrow><mi>t</mi></mrow></msub><mo>+</mo><msub><mrow><mi>δ</mi></mrow><mrow><mi>i</mi></mrow></msub><msub><mrow><mi>u</mi><mi>r</mi><mi>b</mi><mi>v</mi><mi>i</mi><mi>l</mi></mrow><mrow><mi>i</mi></mrow></msub><mo>\times</mo><msub><mrow><mi>p</mi><mi>o</mi><mi>s</mi><mi>t</mi></mrow><mrow><mi>t</mi></mrow></msub><mo>+</mo><msub><mrow><mi>δ</mi><mi>X</mi></mrow><mrow><mi>i</mi><mi>t</mi></mrow></msub><mo>+</mo><msub><mrow><mi>ε</mi></mrow><mrow><mi>i</mi></mrow></msub><mo>,</mo></math> (2)

where

{E n v}_{i t}

${E n v}_{i t}$ equals 1 if there is a specific environmental problem in an urban village i and 0 otherwise;

{u r b v i l}_{i}

${u r b v i l}_{i}$ equals 1 if an urban village i is in the treatment group and 0 otherwise;

{p o s t}_{t}

${p o s t}_{t}$ equals 1 for the second period of 2018 and 0 for the initial period of 2014;

{u r b v i l}_{i} \times {p o s t}_{t}

${u r b v i l}_{i} \times {p o s t}_{t}$ is the DID estimate obtained by interacting the treatment with the post; and

X_{i t}

$X_{i t}$ is a vector of control variables regarding the urban village i characteristics. The subscript t denotes both the 2014 and 2018 periods.

The identification of environmental problems in the PODES dataset is based on the question: “Did a list of environmental problems occur in this village?” The village administrator then responds with either “yes” or “no” to each of the listed environmental issues. Although the list of environmental cases identified in the 2014 and 2018 surveys are different, two types of environmental problems are consistently included in the questionnaire in both years: flooding and open waste burning. On that account, we use these two variables as a proxy for environmental problems. We construct dummy variables for each environmental issue—that is, flooding and open waste burning, where 1 identifies the presence of environmental problems and 0 otherwise.

V. Results and Discussion

Statistical descriptions of the variables of interest in this study, the occurrences of open waste burning and flooding, and the presence of green open space in each urban village are presented in Table 1. In our sample, in 2014, 52.0% of urban villages reported the presence of green open space in their areas. This share increased to 56.4% in 2018. Panel A of Table 1 shows the number of urban villages included in the main estimations—that is, comparing the effect of having green open space in urban villages that did not have any green open space in 2014 but did in 2018 (around 16% of all urban villages) to those urban villages that did not have green open space in both 2014 and 2018 (around 32% of all urban villages). While panel B shows the number of urban villages included in the auxiliary estimations—that is, comparing the effect of losing green open space in urban villages that used to have green open space in 2014 and no longer had any in 2018 (around 12% of all urban villages) to those urban villages that had green open space in both 2014 and 2018 (around 40% of all urban villages). The 12% of urban villages that reported the presence of green open space in 2014 but no longer any presence in 2018 could be due to the rapid expansion of business and residential real estate in metropolitan areas. Such expansions are likely to push the closure of previously existing green open spaces. This variation presents an opportunity to empirically investigate if establishing green open space, rather than its closure, can impact environmental issues differently.

Table 1. **Descriptive Statistics of Main Variables**
	2014			2018
Variables	N	Mean	SD	N	Mean	SD
Urban village with green space	543	0.521	0.500	543	0.564	0.496
Flooding	543	0.344	0.476	543	0.256	0.437
Open waste burning pit	543	0.018	0.135	543	0.197	0.398
Panel A						N
Urban village without green space in 2014 and with green space in 2018 (treatment)						87
Urban village without green space in both 2014 and 2018 (control)						173
Panel B						N
Urban village with green space in 2014 and without green space in 2018 (treatment)						64
Urban village with green space in both 2014 and 2018 (control)						219

$N = sample$ size, $SD = standard$ deviation.

Source: Authors’ compilation.

The descriptive statistics of explanatory variables employed as control variables in the econometric model are provided in Table A1 in Appendix 1. These variables include the inclination to help other people or do social work, the number of places of religious worship, school buildings (e.g., playgroup, kindergarten, elementary school, junior high school, senior high school, and university), day-care facilities, community libraries, supermarkets, restaurants, and hotels. The mutual help activities and number of religious buildings are the proxies of social capital in each urban village. Meanwhile, the number of schools, day-care facilities, and public libraries are measures of educational infrastructure. Last, we consider economic activities in each urban village by considering the availability and the number of supermarkets, restaurants, hotels, motels, and guesthouses.

Tables 2 and 4 display the estimation results of green open spaces’ effect on urban environmental issues. The results of the estimation for the probability of flooding are presented in Table 2, while the results for the likelihood of the open burning of waste are provided in Table 4. We carry out four estimations for each of the models: (i) without regional dummy and control variables, (ii) with regional dummy variable, (iii) with control variables, and (iv) with both regional dummy and control variables. The green open space effect, the main variable of interest, is included as a separate variable in all estimations. The interaction variable Urban village_new×Post indicates the differing effects of the presence of green open space on environmental issues between the treatment group and the comparison group.

Table 2. **Estimation Results on the Impact of Establishing Urban Green Open Spaces on the Probability of Flooding**
(1)	(2)	(3)	(4)	(5)
Constant	0.162***	0.487***	−0.377**	0.127
	(0.0481)	(0.0845)	(0.149)	(0.205)
Urban village (new green space $=$ 1)	0.298***	0.192***	0.285***	0.193***
	(0.0907)	(0.0644)	(0.0814)	(0.0682)
Post (D2018 $=$ 1)	0.0289	0.0289	0.242**	0.171*
	(0.0425)	(0.0427)	(0.104)	(0.0875)
Urban village_new×Post	−0.270***	−0.270***	−0.320***	−0.301***
	(0.0608)	(0.0611)	(0.0682)	(0.0675)
Regional dummy variable	NO	YES	NO	YES
Control variables	NO	NO	YES	YES
Observations	520	520	497	497
R-squared	0.061	0.171	0.130	0.209

Notes: Robust standard errors are clustered at the subdistrict level. *** $p < 0.01$ , ** $p < 0.05$ , and * $p < 0.1$ .

Source: Authors’ calculations based on the 2014 and 2018 Village Potential Census.

A. The Effect of Green Open Spaces on Flooding

The estimation results for the impact of green open spaces on the likelihood of flooding are displayed in Table 2. In these estimations, the treatment group consists of urban villages without green open spaces in 2014 that had them in 2018, while the comparison group consists of urban villages without green open spaces in both 2014 and 2018.

The estimation results without any control variables, as shown in column (2), indicate the statistically significant negative coefficient of the interaction variable (−0.270). This result suggests that establishing green open spaces reduces the likelihood of flooding by roughly 27%. When the regional dummy is included in the estimation, the negative and statistically significant effects remain the same for approximately 27%, as in column (3). When we incorporate control variables in the estimation, as shown in column (4), we also find a statistically significant negative effect on floods. Based on this estimation, establishing new open green areas lowers the likelihood of flooding by 32.5%. When we incorporate regional and control variables in the estimation, as shown in column (5), the results remain consistent. In this regard, the likelihood of flooding in urban villages is reduced by 30.7% due to the establishment of new green open spaces. The probit estimation results, available in Table A2 in Appendix 1, show a similar negative effect of the establishment of green open spaces on flooding.

In the auxiliary estimations, we evaluate if the loss of green open spaces has a negative impact on the likelihood of flooding as a robustness check. The estimation results are presented in Table 3. In this case, the treatment group consists of urban villages that reported having green open spaces in 2014 but losing them by 2018. Urban villages that reported the presence of green open spaces in both 2014 and 2018 are assigned as the comparison group. The interaction variable Urban village_loss× Post represents this effect of losing green open spaces on the probability of flooding.

Table 3. **Estimation Results on the Impact of Losing Urban Green Open Spaces on the Probability of Flooding**
(1)	(2)	(3)	(4)	(5)
Constant	0.452***	0.659***	−0.00601	0.0916
	(0.0611)	(0.0476)	(0.232)	(0.208)
Urban village (losing green space $=$ 1)	−0.140	−0.0385	−0.0786	−0.0251
	(0.0928)	(0.0751)	(0.0789)	(0.0746)
Post (D2018 $=$ 1)	−0.114**	−0.114**	−0.144	−0.127
	(0.0416)	(0.0418)	(0.236)	(0.204)
Urban village_loss×Post	0.00478	0.00478	0.00216	0.00776
	(0.0597)	(0.0601)	(0.0633)	(0.0622)
Regional dummy variable	NO	YES	NO	YES
Control variables	NO	NO	YES	YES
Observations	566	566	561	561
R-squared	0.028	0.249	0.138	0.286

Notes: Robust standard errors are clustered at the subdistrict level. *** $p < 0.01$ and ** $p < 0.05$ .

Source: Authors’ calculations based on the 2014 and 2018 Village Potential Census.

The estimation results show that the loss of green open spaces increases the likelihood of flooding, broadly supporting our earlier findings about how establishing green open spaces lowers the possibility of flooding. These estimations also consider four scenarios: (i) without regional dummy and control variables, (ii) with regional dummy variables, (iii) with control variables, and (iv) with both regional dummy and control variables. The coefficient of the interaction variable in column (2) shows that in the first scenario, with no control variables, the likelihood of flooding rises by 0.4%. This result holds when introducing the regional dummy variable into the estimation model. The likelihood of flooding slightly rises to 0.5% in the model when both the regional dummy and control variables are introduced. Although neither result is statistically significant, the positive sign suggests that the loss of green open spaces has the opposite impact on the likelihood of flooding compared to the establishment of urban open green areas.

B. The Effect of Green Open Spaces on Open Waste Burning

Table 4 displays the DID estimation results for the impact of green open spaces on the likelihood of open waste burning. The treatment group consists of urban villages that reported no green open spaces in 2014 and the presence of green open spaces in 2018, and the comparison group consists of urban villages with no green open spaces in the two periods.

Table 4. **Estimation Results on the Impact of Establishing Urban Green Open Spaces on the Probability of Waste Open Burning**
(1)	(2)	(3)	(4)	(5)
Constant	0.0405	−0.00153	−0.0986	−0.205
	(0.0166)	(0.0887)	(0.148)	(0.179)
Urban village (new green space $=$ 1)	0.289***	0.0681**	−0.000178	0.0368
	(0.0491)	(0.0236)	(0.0259)	(0.0330)
Post (D2018 $=$ 1)	−0.0175	0.289***	0.170	0.173
	(0.0206)	(0.0493)	(0.121)	(0.135)
Urban village_new×Post	−0.128**	−0.128**	−0.134**	−0.108*
	(0.0589)	(0.0592)	(0.0581)	(0.0588)
Regional dummy variable	NO	YES	NO	YES
Control variables	NO	NO	YES	YES
Observations	520	520	497	497
R-squared	0.132	0.228	0.221	0.295

Notes: Robust standard errors are clustered at the subdistrict level. *** $p < 0.01$ , ** $p < 0.05$ , and * $p < 0.1$ .

Source: Authors’ calculations based on the 2014 and 2018 Village Potential Census.

Column (2) in Table 4 shows the estimated result of the model without regional dummy and control variables. The coefficient of the interaction variable in this scenario is negative and statistically significant at the 5% level. The coefficient of −0.128 indicates that establishing green open spaces in urban villages reduces the likelihood of open waste burning by 12.8%. The statistically significant negative result holds in other scenarios: when the regional dummy is included (column 3), when the control variables are included (column 4), and when both the regional and control variables are included (column 5). Incorporating only control variables in the initial model increases the negative effect to 13.5%, whereas adding both the control variables and the regional dummy reduces the negative impact of green open spaces on the likelihood of open waste burning to 10.6%. The probit estimation results, provided in Table A3 in Appendix 1, show similar negative effects of the establishment of green open spaces on the probability of open waste burning.

To check the robustness of the main estimation, we also calculate the effect of losing green open space on the likelihood of open rubbish burning in urban settlements. The estimation results are presented in Table 5. Similar to the earlier robustness check estimation, the treatment group comprises urban villages reporting a loss of green open space, identified by the presence of green open space in 2014 and the reporting of no green open space in 2018. The comparison group comprises urban villages that reported the presence of green open space in both 2014 and 2018. This impact of losing green open space is demonstrated in the interaction variable, Urban village_loss×Post.

Table 5. **Estimation Results on the Impact of Losing Urban Green Open Spaces on the Probability of Waste Open Burning**
(1)	(2)	(3)	(4)	(5)
Constant	0.00457	0.0387	−0.137**	−0.188**
	(0.00483)	(0.0411)	(0.0585)	(0.0723)
Urban village (losing green space $=$ 1)	−0.00457	−0.0157	−0.00310	−0.00483
	(0.00483)	(0.0142)	(0.0100)	(0.0135)
Post (D2018 $=$ 1)	0.128***	0.128***	0.251***	0.306***
	(0.0332)	(0.0334)	(0.0811)	(0.0890)
Urban village_loss×Post	−0.0497	−0.0497	−0.0636	−0.0638
	(0.0446)	(0.0449)	(0.0494)	(0.0485)
Regional dummy variable	NO	YES	NO	YES
Control variables	NO	NO	YES	YES
Observations	566	566	561	561
R-squared	0.063	0.115	0.109	0.152

Notes: Robust standard errors are clustered at the subdistrict level. *** $p < 0.01$ and ** $p < 0.05$ .

Source: Authors’ calculations based on the 2014 and 2018 Village Potential Census.

Our findings generally show a drop in the likelihood of open waste burning when green open space is lost. For instance, column (2) of Table 5 demonstrates that, in the absence of both the regional dummy and control variables, the loss of green open space results in a 4.9% reduction in the likelihood of open waste burning. The same result is obtained when regional dummy and control variables are included separately. But none of these results are statistically significant. We detect a statistically significant negative effect only after including both regional and control variables in the estimation. This finding suggests that the likelihood of open waste burning decreases when green open spaces are lost. This might be due to the conversion of green open spaces into buildings, for example, which increases the pressure on people in the surrounding areas not to burn waste.

Based on the results in Tables 2 and 4, establishing open green areas in urban settings significantly lowers the likelihood of flooding and open waste burning. By accounting for regional and control variables, the estimation results in column (5) of both Tables 2 and 4 are the most reliable. The DID estimation results indicate that the presence of green open spaces reduces the likelihood of flooding by 30.7% and open waste burning by 10.6%. Tables 3 and 5 also present the effect of losing open green areas as robustness checks. While we find consistent results on the probability of flooding, our results indicate inconsistent results in the case of open waste burning. However, this contrasting finding is only significant at the 10% level, indicating a higher probability of errors.

An important identifying assumption of the DID estimation method is the parallel trend assumption. Unfortunately, one of the limitations of this study is that we only have two data points across time. Therefore, it is not feasible to test the parallel trend assumption with such data settings. However, although a formal parallel test is not plausible, we conducted a mean difference test of the urban villages’ characteristics in 2014 and 2018 for the treatment group and the comparison group, both in the case of establishing and losing green open spaces. The results are available in Appendix 1 (Tables A4 and A5). In general, both tables demonstrate that most of the urban villages’ characteristics that went unchanged (indicated by a mean difference that is not statistically significant) and those that changed (indicated by a statistically significance mean difference) are the same for both the treatment group and the comparison group. This indicates that the dynamics of the characteristics of urban villages are the same for both the treated and comparison groups, which then informally supports the argument of parallel trend assumption.

Overall, our findings add to the body of knowledge about the value of green spaces in urban environments, particularly by offering concrete evidence of how Indonesian environmental issues are lessened by the presence of open green areas. However, it is essential to note that establishing green open spaces may not instantly reduce either flooding or open waste burning. In the case of flooding, according to de Groot et al. (2010), creating green open spaces in urban areas can help provide floodwater storage capacity. It can also increase soil permeability, which reduces peak stream flows and surface runoff (Schuch et al. 2017). In this regard, green open spaces would have a lessening effect on the probability of flooding. Meanwhile, in the case of open waste burning, the lessening effect might be explained by the uptake of plant growth that enables the removal sink of carbon dioxide emissions and carbon sequestration (Setälä et al. 2013). These factors might then contribute to the reduction of environmental problems in urban settings.

VI. Conclusions

We estimate the effect of changes in urban green spaces on the likelihood of flooding and open waste burning using data from urban villages in three of Indonesia’s largest cities with the greatest frequency of flooding and the highest levels of air pollution in the country. The estimation results indicate that establishing urban green spaces will have a statistically significant negative impact on the probability of flooding and open waste burning. According to the statistically significant estimation results, the presence of urban open green areas will reduce the likelihood of flooding and the open burning of waste. The results remain consistent when regional variations are taken into consideration. The results continue to hold even after the regional dummy and numerous control variables are added into the estimation. Additionally, the results of the primary estimations are supported by the opposite effect from the robustness tests when considering the case of losing urban green open spaces.

The findings of this study provide empirical evidence that carry important policy implications for countries across Asia and the Pacific. Recognizing the dual benefits of green spaces in mitigating environmental risks and enhancing urban resilience, policymakers may prioritize initiatives aimed at expanding green infrastructure within urban areas. By integrating green open spaces into urban planning frameworks and investing in sustainable landscaping practices, governments can effectively reduce surface runoff during heavy rainfall events, thereby reducing the risk of flooding. Moreover, the promotion of urban green open spaces discourages open waste burning by providing alternative recreational areas for communities. In this way, countries in Asia and the Pacific can adopt holistic policies that not only address immediate environmental challenges but also contribute to the creation of healthier and more resilient urban environments for future generations.

ORCID

Ilmiawan Auwalin https://orcid.org/0000-0003-1520-1190

Rumayya https://orcid.org/0000-0001-8216-3628

Ni Made Sukartini https://orcid.org/0000-0001-8021-0689

Notes

¹ Statistics Indonesia. Greenhouse Gas Emissions by Sector Type, 2000–2019. https://www.bps.go.id/id/statistics-table/1/MjA3MiMx/emisi-gas-rumah-kaca-menurut-jenissektor–ribu-ton-co2e—2000-2019.html (accessed 6 October 2020).

Appendix

Table A1. **Descriptive Statistics of Control Variables**
Variable	Observations	Mean and Proportion	SD	Minimum	Maximum
Mutual help (gotong royong)	1,086	0.991	0.096	0	1
Number of places of worship	1,086	30.290	20.050	2	133
Kindergarten	1,086	6.830	6.157	0	92
Elementary school	1,086	8.634	6.485	0	41
Junior high school	1,086	3.358	2.755	0	16
Senior high school	1,086	3.096	3.027	0	18
University	1,086	0.826	1.232	0	9
Playgroup	1,086	2.118	2.829	0	8
Daycare facilities	1,086	1.031	0.872	0	2
Community library	1,086	1.400	1.373	0	4
Market without building	1,086	0.627	1.445	0	22
Presence of a supermarket	1,058	0.483	0.500	0	1
Number of supermarkets	1,086	5.564	4.468	0	30
Presence of a restaurant	1,086	0.992	0.091	0	1
Number of restaurants	1,086	96.720	127.000	0	1,003
Presence of a hotel, motel, or guesthouse	1,086	0.279	0.449	0	1
Number of hotels, motels, or guesthouses	1,086	1.907	3.433	0	38

SD $=$ standard deviation.

Source: Authors’ calculations.

Table A2. **Probit Estimation Results for the Impact of Establishing Urban Open Green Spaces on the Probability of Flooding**
(1)	(2)	(3)	(4)	(5)
Constant	−0.987***	−0.100	−2.285**	−0.859
	(0.196)	(0.243)	(1.050)	(1.136)
Urban village (new green space $=$ 1)	0.886***	0.672***	0.951***	0.737***
	(0.256)	(0.216)	(0.246)	(0.236)
Post (D2018 $=$ 1)	0.112	0.149	0.914*	0.624
	(0.165)	(0.175)	(0.499)	(0.468)
Urban village_new×Post	−0.788***	−0.940***	−1.102***	−1.118***
	(0.204)	(0.207)	(0.238)	(0.236)
Regional dummy variable	NO	YES	NO	YES
Control variables	NO	NO	YES	YES
Observations	520	520	490	490

Notes: Standard errors are clustered at the subdistrict level. *** $p < 0.01$ , ** $p < 0.05$ and * $p < 0.1$ .

Source: Authors’ calculations based on the 2014 and 2018 Village Potential Census.

Table A3. **Probit Estimation Results for the Impact of Establishing Urban Open Green Spaces on the Probability of Open Waste Burning**
(1)	(2)	(3)	(4)	(5)
Constant	−1.745***	−2.066***	−1.739*	−3.394**
	(0.191)	(0.483)	(0.913)	(1.466)
Urban village (new green space $=$ 1)	−0.250	0.0591	0.0444	0.219
	(0.312)	(0.342)	(0.430)	(0.578)
Post (D2018 $=$ 1)	1.304***	1.444***	0.948	1.516
	(0.188)	(0.223)	(0.807)	(1.209)
Urban village_new×Post	−0.209	−0.0869	−0.446	−0.296
	(0.330)	(0.386)	(0.520)	(0.682)
Regional dummy variable	NO	YES	NO	YES
Control variables	NO	NO	YES	YES
Observations	520	470	497	440

Notes: Standard errors are clustered at the subdistrict level. *** $p < 0.01$ , ** $p < 0.05$ and * $p < 0.1$ .

Source: Authors’ calculations based on the 2014 and 2018 Village Potential Census.

Table A4. **Mean Difference Test of Control Variables for Establishing Green Open Spaces from 2014 to 2018**
	Treatment Group		Comparison Group
Variable	Mean-Diff.	p-value	Mean-Diff.	p-value
Mutual help (gotong royong)	0.00	—	0.94***	0.00064
Number of places of worship	−2.24	0.2681	20.55	0.17893
Kindergarten	−0.51	0.3161	4.26	0.46216
Elementary school	0.98	0.1876	5.38	0.25959
Junior high school	−0.21	0.3096	2.03	0.31031
Senior high school	−0.22	0.3196	1.49	0.50000
University	−0.07	0.3346	0.40	0.16945
Playgroup	2.15***	0.0000	0.51***	0.00000
Daycare facilities	1.47***	0.0000	0.21***	0.00000
Community library	1.33***	0.0000	0.35***	0.00000
Market without building	0.24**	0.0392	0.44	0.17814
Presence of a supermarket	0.91***	0.0000	0.00***	0.00000
Number of supermarkets	0.38	0.2603	3.33	0.04956
Presence of a restaurant	−0.02	0.1572	0.71	0.28166
Number of restaurants	40.13*	0.0514	45.14**	0.02216
Presence of a hotel, motel, or guesthouse	−0.49***	0.0000	0.42***	0.00000
Number of hotels, motels, or guesthouses	−0.29	0.1915	1.54	0.05790

Diff. $=$ difference.

Note: *** $p < 0.01$ , ** $p < 0.05$ and * $p < 0.1$ .

Source: Authors’ calculations based on the 2014 and 2018 Village Potential Census.

Table A5. **Mean Difference Test of Control Variables for Losing Green Open Spaces from 2014 to 2018**
	Treatment Group		Comparison Group
Variable	Mean-Diff.	p-value	Mean-Diff.	p-value
Mutual help (gotong royong)	0.00	—	0.00	—
Number of places of worship	−1.41	0.3061	0.37	0.4254
Kindergarten	−0.77	0.1901	−0.27	0.3083
Elementary school	0.72	0.2128	1.14**	0.0421
Junior high school	0.14	0.3571	0.23	0.2218
Senior high school	0.02	0.4857	0.14	0.3352
University	0.02	0.4748	−0.05	0.3668
Playgroup	2.50***	0.0000	2.30***	0.0000
Daycare facilities	1.42***	0.0000	1.45***	0.0000
Community library	1.45***	0.0000	1.33***	0.0000
Market without building	0.38**	0.0223	0.22**	0.0276
Presence of a supermarket	0.97***	0.0000	0.99***	0.0000
Number of supermarkets	−1.19	0.0376	−1.26	0.0040
Presence of a restaurant	0.02	0.1596	0.00	0.1589
Number of restaurants	44.45***	0.0081	46.28***	0.0004
Presence of a hotel, motel, or guesthouse	−0.64***	0.0000	−0.67***	0.0000
Number of hotels, motels, or guesthouses	−0.38	0.2980	−0.85	0.0110

Diff. $=$ difference.

Note: *** $p < 0.01$ and ** $p < 0.05$ .

Source: Authors’ calculations based on the 2014 and 2018 Village Potential Census.

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Received 12 June 2023

Published: November 7, 2024

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This is an Open Access article published by World Scientific Publishing Company. It is distributed under the terms of the Creative Commons Attribution 3.0 IGO (CC BY 3.0 IGO) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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