The impact of socio-economic development on waste generation: An analysis on European countries

Increasing mechanization with the Industrial Revolution and the resulting rapid production, rapid consumption and resource uses; " thanks to the self-renewal power of nature", has not create the perception that it harms the environment for a long time. However, countries like an EU member, whose economy relies heavily on industry and imports a large part of theirs resources, have realized that the current (linear) economy model cannot be sustainable in the long run. This has highlighted the necessity of a new economic model (circular economy) in the eyes of European countries. The circular economy model, whose importance is increasing day by day; today, it focuses especially on preventing waste. In addition, the evaluation of waste as a value-creating resource; it is a fundamental point that distinguishes the circular economy model from the linear economy model. There are many social and economic factors that affect waste generation. In the study, these factors, which are representative of socioeconomic development; its impact on waste generation has been tested for 26 European countries. The study was carried out using the balanced panel data method and the Driscoll-Kaary resistance estimayor, based on the years 2004-2017. According to the findings obtained; It was determined that the model was significant in general and the independent variables were significant at the 10% significance level. At the same time, the variables that reduce waste generation are the human development index (IGI) and R&D variables, respectively, according to the level of impact; other result is that the GDP variable is a variable that affects the waste generation in an increasing way. According to these results, it is possible to state that if a country attaches importance to its human development and directs R&D studies with rational policies, it will reduce waste generation. Keywords: Waste Generation, European Countries, Socioeconomic Factors, Panel Data Analysis