Waste generation and macroeconomic drivers: a panel study for European countries and regions

Abstract

PurposeThe vast amounts of waste generated today threaten economies and societies due to high environmental and management costs. The aim is to investigate the short- and long-term patterns of municipal waste generation (MWG) in response to socio-economic and demographic growth variables at national and regional levels.Design/methodology/approachA panel data approach employing ordinary least squares (OLS), fixed effects (FE), random effects (RE), fully modified least squares (FMOLS) and error correction model (ECM) techniques. A sample of 28 European countries (2000–2020) and 44 European Union (EU) regions (2000–2018) were selected.FindingsDuring periods of economic growth and higher employment rates, consumer confidence tends to increase, leading to elevated levels of consumer spending and consumption. Intensification in the production factors, specifically capital and employment, results in an upsurge in MWG, thereby creating a cycle where waste generation becomes deeply entrenched in the economic system in both the short and long terms. Rapid population growth, attributed to higher fertility rates, is associated with increased MWG. At the regional level, a double-aging process and a shift toward an aging population exert less pressure on MWG in both the short and long term. Promoting higher levels of environment-oriented human development yields various benefits, including the generation of greater knowledge spillovers, enhanced environmental literacy, a shift toward circular thinking and the promotion of greener entrepreneurship. Increased R&D expenditures facilitate the development of innovative waste reduction technologies, fostering improvements in waste management techniques, recycling processes and the utilization of sustainable materials.Research limitations/implicationsThe research examines the short- and long-term adjustments of MWG in response to changes in macroeconomic variables from low aggregation (countries) to high aggregation (regions). By analyzing the relationship between economic growth, urbanization, healthcare system quality, labor market functioning, demographic trends, educational level, technological advancement and MWG, the study fills a research gap and enhances understanding of waste management interventions. However, data availability and waste statistics accuracy should be considered. Future research could explore the relationship between macroeconomic variables and waste generation in sectors beyond MWG, such as industrial or construction waste, for a more comprehensive understanding of waste generation as a whole.Practical implicationsThe positive correlation between economic activity levels and waste generation in both the short and long terms, emphasizes the criticality of investing in waste reduction and recycling infrastructure to mitigate landfill waste. The negative correlation between population density and waste generation stresses the importance of strategic waste facility placement in low-density areas. To effectively manage higher MWG, tailored waste collection systems and initiatives promoting healthy lifestyles are of immense importance. The positive relationship between employment rates and waste generation underscores the necessity of waste reduction programs that generate employment opportunities. The positive correlation between fertility rates and waste generation emphasizes the need for the expansion of extended producer responsibility programs to include products and materials specifically associated with families and child-rearing. Education campaigns and governmental support for research and development (R&D) in waste reduction technologies are also integral components of an effective waste management strategy.Originality/valueThe short- and long-term adjustments of MWG reacts to shifts in macroeconomic variables from low aggregation (countries) to high aggregation (regions). Previous research has neglected the long-term information contained in variables by not incorporating the lagged error correction term (ETM). Neglecting this aspect could result in imprecise estimates of the elasticities.