Estimating Carbon Emissions from Household Consumption and Practices in Eastern Visayas

Review of Socio-Economic Research and Development Studies 2024 Volume 8 No. 1, 52-86 https://reserds.vsu.edu.ph Research Article ESTIMATING CARBON EMISSIONS FROM HOUSEHOLD CONSUMPTION AND PRACTICES IN EASTERN VISAYAS Gwendolin H. Omalay1 * , Moises Neil V. Seriño1, Lijueraj J. Cuadra1, and Zyra May H. Centino1 1Visayas State University, Baybay City, Leyte, Philippines This study estimates carbon emissions from households in the Eastern Visayas region of the Philippines, examining various sources, consumption, behaviors, and socio-economic factors that influence emissions. The study is guided by the Environmental Kuznets Curve (EKC) framework. Carbon emissions were estimated across three scopes: Scope 1 (stationary combustion and purchased gases), Scope 2 (electricity consumption), and Scope 3 (waste generation and commuting). Scope 1 emissions were identified as the largest contributor. Emissions were analyzed by household characteristics revealing statistically significant differences between provinces, community types, family types, education levels, employment statuses, and income groups. The regression results indicated that income and its squared term, community type, electricity consumption, fuel consumption, and commuting activities using public transportation modes are significant predictors of carbon emissions. The regression analysis confirms the presence of EKC at the household level. This is reflected by the positive sign of income and negative sign of the coefficient for income squared suggesting an inverse U-shaped relationship between income and emissions. Additionally, an assessment of potential net-zero emissions highlighted that current tree-planting efforts are insufficient to offset household emissions significantly. To effectively offset carbon emissions, each household would need to plant at least four trees every month. Lastly, respondents’ awareness, practices, motivations, and perceived barriers were explored and documented. The study cites several recommendations for policy-makers to focus on to effectively reduce household-level carbon emissions. * Corresponding author: Gwendolin H. Omalay, Visayas State University, Baybay City, Leyte, Philippines. E-mail: [email protected] Review of Socio-Economic Research and Development Studies 8(1), 2024 53 Keywords: household carbon emissions, carbon offset, Environmental Kuznets Curve JEL Classification codes: I0, Q2, Q3 1. INTRODUCTION In recent years, the growing recognition of climate change as an existential threat has prompted extensive research into the realm of greenhouse gases and their adverse consequences. As the Earth's climate undergoes significant alterations, understanding the detrimental impacts of greenhouse gas emissions has become paramount. Based on the findings of the 2022 Philippines Country Climate and Development Report, if no action is taken to address climate change, it is projected to have significant economic and human costs. By the year 2040, it is estimated that GDP could potentially decrease by as much as 13.6 percent. This adverse impact is expected to be particularly pronounced among the most disadvantaged households (World Bank 2022). In 2021, it was recorded that 18.1 percent of impoverished Filipinos had per capita incomes insufficient to cover their basic food and non-food needs. This equates to approximately 19.99 million Filipinos living below the poverty threshold (PSA 2022). It is within this context of climate change concern and the need to comprehend the adverse effects of greenhouse gases that this study is undertaken. Carbon emissions, primarily in the form of carbon dioxide (CO2) which makes up 64% of emissions (Buenavista & Tan 2021), have emerged as the foremost driver of climate change (Abeydeera et al 2019). The ever-increasing levels of these emissions in the Earth's atmosphere have led to a rise in global temperatures, triggering a cascade of environmental challenges. The threat of climate change and global warming has brought extreme crises to the planet in many different unprecedented ways possible. As time goes by, these phenomena would eventually worsen and would create greater disaster than what is happening today, that is, if not addressed immediately. Currently, the occurrence of natural disasters has been recorded three times more frequently compared to the last 50 years (UN News 2021). In response to the escalating climate crisis, international efforts have intensified to combat climate change, aligning with the commitments outlined in the PH NDCs for 2021. It outlines specific targets for reducing greenhouse gas emissions. Recognizing the urgency of climate action, addressing climate change Ndlovu: The Impact of Property Rights on Foreign Direct Investment 54 is not solely an environmental concern but also integral to achieving five specific Sustainable Development Goals related to environmental sustainability, economic growth, and urban development. These include SDG 7, 11, 12, 13, and 15 (United Nations 2022). The Intergovernmental Panel for Climate Change attested that global warming caused by human activities has increased by about 1.0°C (0.8~1.2°C) as compared to the pre-industrial level and if current trends continue, this level will possibly reach 1.5 °C between 2030 and 2052 (IPCC 2021). Households play a significant role in global greenhouse gas emissions, accounting for approximately 72% of the total (Dubois et al 2019). According to Serio (2016), failing to address household emissions could imperil global attempts to stabilize the climate system, owing to the accrued carbon emissions stemming from domestic consumption. Within this context, it is crucial to recognize that households, often overlooked in the discourse on climate change, are significant contributors to carbon emissions. These emissions stem from various facets of household consumption and practices, encompassing energy consumption, transportation choices, waste generation, and more. However, despite their noteworthy contribution, studies focusing on quantifying carbon emissions at the household level remain notably scarce. In light of this research gap, this study is particularly motivated by the need to understand and estimate carbon emissions at the household level in Eastern Visayas – an ideal area for this study due to its unique characteristics. This region, comprising several islands in the Philippines, offers a compelling context for understanding household-level carbon emissions. Eastern Visayas boasts diverse environments, from coastal regions to upland communities, with a mix of urban centers and rural areas, allowing us to comprehensively examine carbon emissions in various settings. Furthermore, the region is vulnerable to climate change, facing risks like sea-level rise and extreme weather events, making it a critical area for studying emissions and devising climate-resilient strategies. This study is pivotal in climate change mitigation efforts by estimating household carbon emissions from consumption patterns, guiding targeted interventions. By pinpointing high-emission areas, it addresses climate change's urgency globally. The research emphasizes individual and collective household responsibility - that change starts at home, and demonstrates how household involvement can have a meaningful impact on combating climate change by making informed choices, promoting sustainable behaviors to reduce carbon footprints and raising awareness of environmental consequences of carbon Review of Socio-Economic Research and Development Studies 8(1), 2024 55 emission. Such efforts not only cut costs but also enhance community well-being. Aligning with national priorities like the Philippines' NDCs and SDGs, it contributes to the body of knowledge, methodologies, and insights into household carbon emissions, fostering further research and interdisciplinary collaboration against climate change. Practical implications extend to policymakers shaping sustainable policies, environmental groups leveraging insights for public awareness and advocacy. Researchers can build upon the study's findings to advance knowledge in this field. The study aims to understand how household activities and consumption patterns contribute to carbon emissions in the Eastern Visayas region of the Philippines. It seeks to estimate household carbon emissions and identify the main sources, such as energy use, transportation, and waste. Additionally, the study explores differences in emissions across various households and assesses the potential for achieving net-zero emissions. It also seeks to understand household views on nature-based solutions. Ultimately, the study provides recommendations for policymakers to encourage sustainable practices and reduce emissions. 2. THEORETICAL AND CONCEPTUAL FRAMEWORK This study is guided by the Environmental Kuznets Curve (EKC) hypothesis, which extends Simon Kuznets' 1955 theory. Kuznets proposed that as per capita income increases, income inequality initially rises and then decreases, forming an inverted U-shaped curve. In 1991, this concept was applied to environmental quality, suggesting that as countries develop economically, environmental degradation may initially worsen but then improve. This led to the development of the EKC, illustrating the relationship between environmental degradation and per capita income (Yandle et al., 2004). The EKC hypothesis emerged through the research of Grossman and Krueger (1991), who argued that economic activity does not inevitably harm the environment; instead, as incomes rise, the demand for environmental improvements and the resources for investment increase. Beckerman (1992) supported this, stating that while economic growth may initially lead to environmental degradation, ultimately, wealth is necessary for environmental improvement. Grossman and Krueger (1991, 1994) provided empirical evidence that the relationship between per capita income and environmental degradation follows an inverted U-shape (as cited in Beyene & Kotosz, 2019). Ndlovu: The Impact of Property Rights on Foreign Direct Investment 56 Figure 1 illustrates the graphical representation of the hypothesis in the form of an inverted U-shaped curve. In this representation, environmental degradation serves as the dependent variable and can be measured through various indicators such as pollutants (including air, water, and soil pollution, or deforestation), in this study’s case, we focus on carbon emissions. On the other hand, per capita income is the independent variable. To test the hypothesis of EKC, the study will have to calculate income per capita and carbon emissions per capita. To do this, begin by computing the gross income of households, then, divide the total household income by the number of members in each household to obtain the per capita income. The formula for income per capita is: 𝐼𝑛𝑐𝑜𝑚𝑒 𝑝𝑒𝑟 𝐶𝑎𝑝𝑖𝑡𝑎 = 𝑇𝑜𝑡𝑎𝑙 𝐻𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑 𝐼𝑛𝑐𝑜𝑚𝑒/𝐻𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑 𝑆𝑖𝑧𝑒 (1) Next, to estimate carbon emissions per capita within a household, divide the total carbon emissions per household by the household size. This approach allows us to estimate the average carbon emissions per person within a household. The formula for carbon emission per capita is: Figure 1. A typical EKC diagram (source: Yandle et al, 2004) Review of Socio-Economic Research and Development Studies 8(1), 2024 57 𝐶𝑎𝑟𝑏𝑜𝑛 𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠 𝑝𝑒𝑟 𝐶𝑎𝑝𝑖𝑡𝑎 = 𝑇𝑜𝑡𝑎𝑙 𝐻𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑 𝐶𝑎𝑟𝑏𝑜𝑛 𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛 /𝐻𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑 𝑆𝑖𝑧𝑒 (2) Once income per capita and carbon emissions per capita within households are computed, the relationship between these two can be visualized on a graph. On the graph, income per capita is plotted on the x-axis, and carbon emissions per capita are plotted on the y-axis. Each data point on the graph represents a household. This visualization helps reveal the potential presence of the EKC hypothesis within the study's data. 3. METHODOLOGY This study uses data from the ENHANCE Project conducted in the six provinces of Region VIII – Eastern Visayas: Biliran, Samar, Eastern Samar, Northern Samar, Leyte, and Southern Leyte. Primary data were collected using a semi-structured survey questionnaire, which included both close-ended and open-ended questions to thoroughly capture information on carbon-emitting domestic consumption and practices. A pre-test of the survey instrument was conducted prior to data collection to ensure its validity and suitability for the study's objectives. Data were collected from March to August 2022, with successful interviews conducted with 360 households. Using proportional sampling, 302 of these responses were utilized. The remaining surveys were conducted from March to April 2024 in Leyte, Western Samar, and Northern Samar to complete the sample size of 385, as determined using Cochran's formula. Table 1. Proportional sampling of respondents by province in Eastern Visayas. Location 2020 Total population by province Proportional percentage Calculated no of respondent No of interview already conducted No of interview conducted Biliran 179,312 4% 15 5 (excess 35) 0 Eastern Samar 477,168 11% 41 50 (excess 9) 0 Leyte 2,028,728 45% 172 110 62 Northern Samar 639,186 14% 54 50 4 Samar (Western Samar) 793,183 17% 67 50 17 Southern Leyte 429,573 9% 36 50 (excess 14) 0 Total 4,547,150 100% 385 302 83 Ndlovu: The Impact of Property Rights on Foreign Direct Investment 58 Data analysis The data analysis for this study will adopt a mixed-methods approach, incorporating both qualitative and quantitative methods to provide a comprehensive understanding of household-level carbon emissions using Microsoft Excel and STATA. Employing descriptive statistics, carbon emission factors and regression analysis are instrumental in serving the study’s objectives to estimate carbon emissions and determine significant variables influencing carbon emission. Carbon Emission Factors are sourced from the 2024 GHG Emission Factors Hub by the United States Environmental Protection Agency. The carbon emission factor which is always expressed as a ratio is the average emission rate of carbon dioxide associated with that particular activity or consumption (Climate Change Commission 2015). Carbon emissions from different categories of household consumption and practices can be computed using the general quantification equation, suggested by the Climate Change Commission, expressed as follows: 𝐶𝑂2= 𝐴𝑐𝑡𝑖𝑣𝑖𝑡𝑦 𝑜𝑟 𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 × 𝐶𝑎𝑟𝑏𝑜𝑛 𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛 𝐹𝑎𝑐𝑡𝑜𝑟 (3) In estimating carbon emissions from household practices, three distinct scopes are considered. Scope 1 includes emissions resulting from stationary combustion and purchased gases. Scope 2 covers emissions from electricity consumed. Lastly, Scope 3 accounts for emissions generated from waste and commuting activities using public transportation. Carbon emission factors (Table 2) used in the calculation of these emissions are from the 2024 GHG Emission Factors Hub by the United States Environmental Protection Agency. Table 2. Carbon emission factors. Variable Fuel type Co2 Factor Unit SCOPE 1 1a. Stationary combustion Wood and Wood Residuals 1,640 kgCO2/short ton Liquefied Petroleum Gas (LPG) 5.68 kgCO2/gallon Butane 6.67 kgCO2/gallon 1b. Purchased gases Kerosene 10.15 kg CO2/gallon Gasoline 8.78 kg CO2/gallon Diesel 10.21 kg CO2/gallon Review of Socio-Economic Research and Development Studies 8(1), 2024 59 Source: US EPA, 2024 GHG emissions factors hub Regression model To identify significant variables, the regression equation is expressed as follows: 𝑙𝑜𝑔𝐶𝑂2= 𝛽0+ 𝛽1𝑙𝑜𝑔𝐼𝑁𝐶 + 𝛽2𝑙𝑜𝑔𝐼𝑁𝐶𝑆𝑂 + 𝛽3𝐹𝑇 + 𝛽4𝐶𝑇 + 𝛽5𝐸𝐷𝑈𝐶 + 𝛽6𝐶𝐶𝐴 + 𝛽7𝐾𝑊𝐻 + 𝛽8𝐹𝑈𝐸𝐿𝑆 + 𝛽9𝑊𝐺 + 𝛽10𝑃𝑇 + 𝜖 (4) Where the log of the total household carbon emissions is the dependent variable on the left-hand side and variables on the right-hand side such as log of Income, log of Income2, Family Type, Community Type, Education, Climate Change Awareness, Electricity Consumption, Fuels, Waste Generation, Public Transport, are the independent variables. 4. RESULTS AND DISCUSSION Demographic profile of respondents The data covers five provinces of Eastern Visayas, with Leyte having the highest frequency at 172 respondents, making up 44.68% of the sample. The respondents were spread across different ecological settings, with rural areas having the highest representation at 206 respondents. Table 3. Distribution of households by province in Eastern Visayas. SCOPE 2. Electricity consumed Megawatt hour 0.7122 tCO2/MWH SCOPE 3 3a. Wastes generated (Landfilled materials) Steel Cans 0.02 tCO2/short ton Glass 0.02 tCO2/short ton Mixed Paper 0.89 tCO2/short ton Mixed Plastics 0.02 tCO2/short ton Food Waste 0.68 tCO2/short ton 3b. Commuting via Public Transportation Passenger Car 0.1752 kgCO2/mile Motorcycle 0.3767 kgCO2/mile Bus 0.0707 kgCO2/mile Province Frequency Percent Biliran 15 3.9 Eastern Samar 41 10.65 Ndlovu: The Impact of Property Rights on Foreign Direct Investment 60 Table 4. Distribution of households by community type. Most respondents are female, accounting for 80.52% of the sample. The reason is twofold. Firstly, women as housewives most of the time stay at home while husbands are typically at work. This leads to a situation where women are more available to participate in the survey. Secondly and accordingly, women often have firsthand knowledge as they handle a lot of domestic activities within households so they are more likely able to answer the survey than men. The majority of respondents fall between the ages 30 to 69, which is 74.54% of the respondents. The average age of respondents is approximately 46 years. The largest portion of respondents are married (67.27%) and are Roman Catholics (88.83%). For educational attainment, 40.52% of respondents have reached college level or above, 37.92% have reached high school and 21.56% reached the elementary level. Meanwhile, most respondents (62.34%) are employed. Table 5. Summary statistics of respondents' profile. Leyte 172 44.68 Northern Samar 54 14.03 Samar 67 17.4 Southern Leyte 36 9.35 Total 385 100 Community Location (Ecological setting) Type Coastal Lowland Upland Total Rural 82 42 82 206 Urban 46 97 36 179 Total 128 139 118 385 Demographic characteristics Frequency Percent Sex Female 310 80.52 Male 75 19.48 Age Range 10-29 72 18.7 30-49 151 39.22 50-69 136 35.32 70-89 26 6.75 Civil Status Live-in 3 0.78 Married 259 67.27 Separated 7 1.82 Single 89 23.12 Widow 27 7.01 Review of Socio-Economic Research and Development Studies 8(1), 2024 67 lower transportation-related emissions. Additionally, rural households might rely more on local food production and consumption, further reducing the need for transportation-related emissions. In contrast, urban residents benefit from more accessible and affordable public transportation options. This convenience encourages more frequent travel for shopping, education, dining, and leisure activities, leading to higher transportation emissions. The frequent use of transportation in urban areas thus contributes to the higher mean emissions observed. This comparison underscores the significant impact of transportation availability and lifestyle choices on overall carbon emissions between urban and rural areas. The amount of CO2 emissions by household family type reveals intriguing disparities in carbon footprints. While nuclear families exhibit a higher total emission compared to extended families, extended families have a higher mean emission per household. The difference between groups is statistically significant (Prob > |z| = 0.0019***, Table 11). This suggests that while extended families collectively contribute less to the overall emissions compared to nuclear families, individual households within the extended family setup have a higher average emission. This difference could be attributed to various factors such as household size, consumption patterns, and lifestyle choices. Nuclear families may have a smaller total emission due to their smaller household size, but individual households within extended families may have higher emissions per household due to shared resources and potentially higher energy needs. In terms of educational attainment, while there is some variation in emissions across different education levels, the differences are not as pronounced. Households with members attaining college-level and above education tend to have slightly higher mean emissions compared to those with elementary or high school-level education. This aligns with the expectation that higher education levels are often associated with higher income levels and potentially more affluent lifestyles, which can lead to increased consumption and energy usage. When categorizing education into two elementary and high schools and above, a statistically significant difference is observed (Prob > |z| = 0.0018***, Table 11). Similarly, employed individuals tend to have higher mean emissions compared to unemployed individuals, reflecting the influence of economic activity and lifestyle choices on carbon emissions. The difference between groups is statistically significant (Prob > |z| = 0.0129**, Table 11). Lastly, there is significant difference in carbon emissions between income groups (1 & 2) (Prob > |z| = 0.0000***, Table 11). Income Group 1 shows a higher Ndlovu: The Impact of Property Rights on Foreign Direct Investment 68 total sum of emissions (69.04643), while Income Group 2 exhibits a higher mean emission rate (0.3329922). This suggests that although Income Group 1 emits more in total, individuals in Income Group 2 emit more per capita on average. The data aligns with the EKC hypothesis, suggesting that as incomes rise, average emissions per capita initially increase due to increased consumption before potentially declining with further economic development and environmental policy implementation. Table 10. Test of differences: one-way ANOVA. Variables F Prob > F Is there a statistically significant difference in CO2 emissions between group means? Province 2.81 0.0165** YES Location Ecological Setting 1.20 0.3011 NO Source: Author’s Estimation using Stata 14. *** p<0.01, ** p<0.05, * p<0.1 Table 11. Test of differences: Rank-Sum (Mann-Whitney U) test. Variables Z Prob > |Z| Is there a statistically significant difference in CO2 emissions between groups? Community Type -2.0010 0.0454 ** YES Civil Status -1.5160 0.1296 NO Family Type -3.1100 0.0019 *** YES Sex 1.6050 0.1085 NO Education -3.1190 0.0018 *** YES Employment Status -2.4860 0.0129 ** YES Income Group -4.4660 0.0000 *** YES Religion 0.2460 0.8059 NO Source: Author’s Estimation using Stata 14. *** p<0.01, ** p<0.05, * p<0.1 Existence of the Environmental Kuznets Curve To examine the existence of EKC, let's begin by visualizing the data. First, let's analyze the data in terms of income and carbon emissions per capita. Examining distinct income groups can help validate the EKC hypothesis by revealing how environmental impacts vary at different stages of economic development. Income groups were defined based on methods from the Philippine Institute for Development Studies (2022), Consequently, three income groups were identified. Households earning below poverty threshold (PHP 13,797) are Review of Socio-Economic Research and Development Studies 8(1), 2024 69 categorized as poor or low-income. Middle-income households are defined as those earning between two to twelve times the poverty threshold, i.e., between ₱27,594 and ₱165,564 per month. High-income households are those earning above PHP 165,564. Notably, no high-income households were present in the sample. The lowest-income group exhibits lower mean per capita emissions at 0.04tCO2 compared to 0.07 tCO2 for the middle-income group (Table 12). The scatterplot’s fitted curve shows a pattern consistent with the initial stages of the EKC hypothesis, indicating that per capita carbon emissions tend to increase with income. Table 12. Summary of Income Per Capita and Carbon Emission Per Capita by Income Group Table 12. Summary of income per capita and carbon emission per capita by income group. Income group Frequency Income per capita CO2 emissions per capita 1 351 1695.952 0.0433467 2 34 9380.673 0.0674329 3 0 - - However, due to the absence of households in the high-income group in our sample, the scatterplot does not capture emissions dynamics at higher income levels. Thus, based solely on the scatterplot, we cannot confidently determine whether the later phase of the EKC, where emissions decline after a certain point as income continues to grow, exists. Therefore, further data including higher income groups is necessary to conclusively confirm this pattern. Ndlovu: The Impact of Property Rights on Foreign Direct Investment 70 Figure 3. The environment Kuznet curve. The visualization of EKC using aggregate data did not fully confirm the pattern, prompting further investigation. Therefore, disaggregation by community type and education level was performed to assess whether EKC trends become more evident or reliable. As can be observed in the scatterplots (Figure 6-7), both exhibit a similar pattern to the overall visualization of carbon emissions, even without disaggregation. Review of Socio-Economic Research and Development Studies 8(1), 2024 71 Figure 4. EKC by community type. Figure 5. EKC by education level. Regression Analysis To further explore the EKC, regression analysis was conducted across three model iterations. Model 1 established a baseline relationship between Ndlovu: The Impact of Property Rights on Foreign Direct Investment 72 log_INC, log_INCSQ, and the dependent variable log_CO2 without considering additional factors. However, neither log_INC nor log_INCSQ were statistically significant in Model 1, suggesting their impact on explaining variation in the dependent variable is limited without accounting for other factors. Although income variables are not significant in this model, the result still aligns with the EKC hypothesis, as there is a positive coefficient for the log_INC and a negative coefficient for the log_INCSQ. Model 2 extended the analysis by including additional explanatory variables. Here, both log_INC and log_INCSQ became significant, indicating their effects are detectable when controlling for these additional factors. Results for Model 2 with significant positive coefficient for log_INC and negative coefficient for log_INCSQ suggest the existence of the Environmental Kuznets Curve (EKC) at the household level depicting an inverse U-shaped typed of relation with carbon emissions. This suggests that an increase in household income is associated with a reduction in emissions in the long run. Additionally, variables such as CT (urban living), KWH (electricity consumption), FUELS (fuel consumption), and PT (public transportation) were found to be statistically significant. The same is true in Model 3 with province specific fixed effects to control for unobserved heterogeneity across 6 provinces, showing that the same variables remain significant, indicating a reliable relationship. Despite minor variations in coefficients due to the fixed effects absorbing some of the variations that would otherwise be explained by the independent variables, the significant results for log_INC and log_INCSQ persisted. This consistency across models provides robust evidence of the presence of EKC. Results suggests that as households in Region 8 become more affluent the carbon emission is expected to reduce. This implies that households are getting richer their choices are becoming more environment friendly as reflected in the reduction in emission. Overall, the model explains a substantial portion of the variation in carbon emissions (R-squared = 0.74) and controls for province-specific factors, ensuring that the relationships are robust and not driven by unobserved heterogeneity across provinces. Table 13. Regression models. Dependent variable: LOG_CO2 Independent variables Model 1 (Baseline) Model 2 (Pooled OLS) Model 3 (Fixed effects - Province) log_INC 0.715 0.835 *** 0.836 *** Review of Socio-Economic Research and Development Studies 8(1), 2024 73 (0.562) (0.319) (0.321) log_INCSQ -0.0234 -0.0425 ** -0.0423 ** (0.0313) (0.0178) (0.0179) FT 0.0673 0.0739 (0.0447) (0.0451) CT 0.136 *** 0.138 *** (0.0404) (0.041) EDUC 0.0377 0.0296 (0.0518) (0.0524) CCA 0.0401 0.034 (0.0652) (0.0661) KWH 0.00472 *** 0.00467 *** (0.00044) (0.00044) FUELS 0.017 *** 0.0172 *** (0.00069) (0.00072) WG -0.00043 -0.00043 (0.00116) (0.00116) PT 0.001 *** 0.00101 *** (0.00012) (0.00012) Constant -6.278 ** -6.794 *** -6.81 *** (2.504) (1.433) (1.441) Observations 372 372 372 R-squared 0.143 0.744 0.744 No. of provinces 6 Source: Author’s Estimation using Stata 14. Note: Standard errors are in parentheses. Other values represent coefficients. *** p<0.01, ** p<0.05, * p<0.1 Interpretation of the Key Regression Coefficients A 1% increase in income is associated with an approximate 0.836% increase in total carbon emissions, holding other factors constant. The high significance level (p < 0.01) indicates a strong positive relationship between income and carbon emissions. The negative coefficient for the squared term of log income suggests a diminishing marginal effect of income on carbon emissions. Simply put, Ndlovu: The Impact of Property Rights on Foreign Direct Investment 74 the rate of increase in carbon emissions diminishes as income increases. This coefficient is significant at the 0.05 level (p < 0.05). Living in an urban community (CT) is associated with a 0.138% increase in total carbon emissions, holding other factors constant. This variable is highly significant (p < 0.01), indicating a strong positive association between urban and carbon emissions. For each additional unit of electricity consumption (KWH), total carbon emissions increase by approximately 0.467%, holding other factors constant. This relationship is highly significant (p < 0.01), showing a positive impact of electricity consumption on carbon emissions. Each additional liter of fuel consumption is associated with a 1.72% increase in total carbon emissions, holding other factors constant. This variable is highly significant (p < 0.01), indicating a positive relationship between fuel consumption and carbon emissions. Each additional kilometer traveled using public transportation is associated with a 0.101% increase in total carbon emissions, holding other factors constant. This relationship is highly significant (p < 0.01), suggesting a positive impact of public transportation usage on carbon emissions. The constant term of -6.81 represents the baseline level of emissions when all the independent variables are minimal or zero. The coefficient is highly significant (p < 0.01), which means that this intercept term is statistically significant. The negative constant term does not directly indicate a decrease in emissions. Instead, it indicates a very low level of emissions when the log-transformed value is backtransformed (exponentiated). For example, 𝑒-6.81 ≈ 0.00109. It represents a very small positive number, indicating very low baseline emissions. The Potential of Net Zero Emission Among Households In the overall assessment, the calculated total emissions amount to 80.37 tCO2/month or 964.42 tCO2 annually. Normalizing these emissions by household, the average emissions per household is about 0.21 tCO2 per month or 2.51 tCO2 annually. Similarly, when normalized by capita, the average emissions per capita stood at 0.04 tCO2 per month or 0.50 tCO2 annually. Table 14. Total annual and monthly emissions. Monthly estimates Annual estimates Review of Socio-Economic Research and Development Studies 8(1), 2024 75 kgCO2 tCO2 kgCO2 tCO2 Total CO2 80,368.1670 80.3682 96,4418.0036 964.4180 CO2 per HH 208.7485 0.2087 2,504.9818 2.5050 CO2 per Capita 40.16400148 0.0417 481.9680 0.5010 According to the International Energy Agency (2023), the global CO2 emissions per capita in 2021 were 4.3 tCO2, and for the Philippines, it was 1.2 tCO2. Meanwhile, a study on community-level carbon emission quantification found that an individual emits 685.26 kgCO2 per year (GC AAB FPH 2024). These figures are higher than the estimated CO2 per capita emission in this study at 481.97 kgCO2 or 0.5 tCO2 annually which possibly reflects localized factors. Eastern Visayas, has a lower population density and is just a subset of the Philippine population. Furthermore, our estimates focus solely on household consumption and practices, excluding industrial or commercial activities that contribute to higher per capita emissions. This results in emissions that are significantly lower compared to community-level, broader national, or global averages. To identify the potential for net zero emissions among households, an analysis of CO2 sequestration was conducted through the tree planting initiatives that the households participated in over the past 5 years. A total of 2,877 trees were planted, with each tree sequestering approximately 0.06 tCO2, resulting in the sequestration of 172.62 tCO2. Taking into consideration that not all planted trees grew or survived, based on the Department of Environment and Natural Resources report, which indicated a 78% survival rate of trees planted under the NGP from 2011 to 2016 (PIDS 2023) we can estimate the effective CO2 sequestration. Out of 2,877 trees, we assume that only 78%, or 2,244 trees grew, and sequestered CO2 at the same rate of 0.06 tCO2 per tree. Therefore, the effective CO2 sequestration was reduced to 134.64 tCO2. Table 15. Carbon sequestration. Sequestration activity No. of trees planted tCO2 sequestered/tree Total tCO2 sequestered Tree Planting 2,877 0.06 172.6200 Tree Planting 2,244 0.06 134.6436 Given these estimates, the five-year tree planting effort offset slightly more than a month's worth of the region's total carbon emissions. While tree planting could have a greater impact on carbon sequestration, the current efforts Ndlovu: The Impact of Property Rights on Foreign Direct Investment 76 by households are not enough to offset the scale of emissions they produce. However, the estimation of carbon offsets above could be underestimated. There are several domestic activities such as participation in environmental conservation/restoration, gardening, waste reduction, etc., which were documented but unfortunately not quantified in this study. These activities also contribute to reducing the carbon footprint and could result in additional offsets not accounted for in the current estimates. To effectively offset the region's monthly carbon emission, the 385 respondents altogether must plant at least 20,607 trees every year. This translates to each household needing to plant at least 54 trees, or at least 11 trees per person each month (Table 17). The study’s estimates are lower than those from the GHG Emissions Inventory 2023-2024 of Barangay Cogon in Ormoc City conducted under the Ako Ang Bukas (AAB) Program of Green Convergence, which determined that a total of 28,126 trees are needed to offset carbon emissions from 287 households, equating to about 98 trees per household. Table 16. Required number of trees for planting to offset carbon emissions. Total emission (per region, household, and per capita) Number of trees required for planting (Effective Sequestration Rate: 78% survival rate x 6% sequestration rate = 4.68%) Monthly Annual Total CO2 1,717 20,607 CO2 per HH 4 54 CO2 per Capita 1 11 Perceptions of Households on Nature-Based Solutions The data highlights significant awareness gaps and varying levels of engagement in sustainable practices among households in the region. While 87.79% are aware of climate change, only 32.73% are familiar with Nature-Based Solutions (NBS), indicating a need for targeted education on sustainable practices. Furthermore, 80.52% recognize CO2 emissions as a contributing factor, and 63.12% of households acknowledge that their activities contribute to CO2 emissions. Transportation is perceived as the largest contributor to CO2 emissions, followed by kitchen activities and waste management. 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