## 1 Introduction
Studies from Vietnam reported that higher-income families produce less SW and organic fraction per household but more paper and plastic. They also reported that as the education level and family size increase, the household SW generation rate increases [23]. Family size has been shown to have positive correlation with the SW generation rate in India [15]. However, researchers observed that families with median income generated a maximum amount of SW. A study from the agro-industrial region of Podlasie province in northeastern part of Poland revealed that gender, age, and employment rate could influence municipal waste generation rate. The study showed that women produce more waste than men, and the largest contributor is the working-age group from 14 to 64 years. The more employed women in this group,the less the waste generation rate can be expected [24]. In another recent study, the artificial neural network (ANN) was used to analyze demographic and socio-economic data from 220 municipalities in Ontario, Canada. The researchers reported that populations with 45+ years of age have a negative correlation with MSW; thus, the older population creates less waste. However, income and employment have a positive correlation [25]. Emission from waste is heavily influenced by behaviors of the economic and consumers of the society, and they are distinct from other emissions (i.e., SO\({}_{2}\), PM) which are production- and activity-based. [PERSON] et al. [19] used the Granger causality test to identify the causal relationship among waste generation, waste emissions, recovery waste generation, and GDP. They reported no causality between GDP and waste generation, but total waste and recycling generation significantly cause positive and negative greenhouse gas emissions from the waste sector, respectively. On the other hand, [PERSON] et al. [26] reported evidence supporting an inverted U-shaped relationship between waste emission and GDP using 20 years of (1985-2005) data from 29 Chinese provinces. [PERSON] and [PERSON] [27] investigated the relationship between waste emission (i.e., CH\({}_{4}\)) and GDP per capita for G7 countries. They reported no evidence for the traditional environmental Kuznets curve (EKC) hypothesis and observed an inverted N-shaped curve between CH\({}_{4}\) emission and GDP per capita. The study recommends that increasing GDP per capita should be aligned with strong policies for sustainable consumption and production and increasing the efficiency of resources will reduce waste emission. The municipal solid waste management operation usually consists of several sequential activities after generation, which include collection, transportation, and treatment [28]. All these activities from generation to treatment are significant sources of GHG emission [29]). Biodegradable organics, which occupies a significant portion of MSW, could increase GHG emissions. Globally, the waste management sector generates approximately 1.3 Gt CO\({}_{2}\) e every year. This sector is responsible for about 2.8% of total emissions of GHG [3]. The organic degradable segment of MSW usually derives from consumed vegetable and fruit [30]. All over the world landfills are the most common practice for solid MSW disposal [31]. Organic components in the lower layers of open dumps and landfills are decomposed by the anaerobic process, generating landfill gas (LFG) containing about 50-60% methane [32]. The rate of methane production depends on waste characteristics, moisture content, quantity, age, and oxygen availability [33]. Methane has a shorter atmospheric life span and in terms of global warming potentials (GWP) is 21 times stronger than CO\({}_{2}\). Landfills are one of the largest anthropogenic sources of methane emission [34]. According to the United States Environmental Protection Agency (USEPA) methane emissions from landfill in Asia, Latin America, and Africa, is equivalent to 37 million metric tons of CO\({}_{2}\), which is equivalent to emissions from more than 102 million automobiles [32].