Public perception of waste regulations implementation. Natural language processing vs real GHG emission reduction modeling

Abstract

The problem with waste generation and waste treatment that countries worldwide are facing, even after the implementation of measures, raises the question of the adequacy and viability of the regulations. The waste sector has been shown to contribute to a most notable increase in greenhouse gas (GHG) emissions in many countries worldwide, mainly coming from solid waste disposal and suggesting the need for a more profound observation of the waste management application. Therefore, this study employs an integrated approach to analyze the environmental, economic and social aspects of waste management, as the three fundamental pillars of sustainable development. Based on the Intergovernmental Panel on Climate Change (IPCC) methodology, a unique model was developed for the projection of GHG emissions in different scenarios, to evaluate the impact and cost of proposed waste policies and measures adopted to the reduction of greenhouse gas emissions, as well as the extent to which citizens themselves can contribute to the achievement of the goals. Furthermore, NLP (Natural Language Processing) methods were used to draw conclusions about the public opinion related to the waste sector, expressed by citizens and news media and measured on Twitter and news media teasers' data. The results show that more than 70% of the emissions can be reduced by 2050 with a significant contribution from the citizens, if all proposed measures are implemented, when compared to a scenario without policies and measures. The results also suggest a strong correlation between news reports and waste-related tweets, confirming an alignment in public perceptions and a response connected to waste management policies. The conclusions of this study offer valuable implications for policy-makers, suggesting the continuous observation and inclusion of citizens in the process of waste regulations implementation and effect.