Evaluation models for the impact of pricing factor on environmental performance in different countries

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The need to increase the price of non-green, carbon-emitting goods, as well as the application of new environmental taxes and fees to help solving the global climate crisis, has been actively discussed. However, price is not only a strong impetus for market development, but it can also restrain growth. The price level and population purchasing power belong to the key indexes that define the market capacities in different countries. This paper aims to investigate the impact of income inequality, including price levels and purchasing power, on environmental performance in different countries. The research method is based on RapidMiner’s machine learning programs, applying three modeling algorithms: correlation, clustering, and decision trees with a static index database of more than 150 countries around the world. The results obtained partially confirm the conclusions made by other researchers studying the Environmental Kuznets concept (EKC) effects. In particular, it was found that an important factor influencing the efficiency of the environment in the country’s ecosystem is the level of population’s income. The analysis also shows that environmental performance is strongly dependent on domestic price levels. This may support the hypothesis that the cost of green goods reflects a high benchmark for natural resource costs. However, further research is needed, including such directions as sources of financing for the implementation of circular projects, as well as the associated economic and environmental effects.

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    • Figure 1. Model for assessing the impact of price levels and purchasing power on environmental performance in different countries
    • Figure 2. K-means model of clusterization of countries by EPI
    • Figure 3. Decision tree for classification of environmental performance cluster
    • Table 1. Key indicators
    • Table 2. Correlation matrix
    • Table A1. Clusters of countries by EPI index
    • Conceptualization
      Viktoriia Apalkova, Sergiy Tsyganov, Nataliia Meshko
    • Data curation
      Viktoriia Apalkova, Nadiia Tsyganova
    • Formal Analysis
      Viktoriia Apalkova, Sergiy Tsyganov, Nataliia Meshko, Nadiia Tsyganova
    • Funding acquisition
      Viktoriia Apalkova, Sergiy Tsyganov, Nataliia Meshko, Nadiia Tsyganova, Serhii Apalkov
    • Investigation
      Viktoriia Apalkova, Nadiia Tsyganova
    • Methodology
      Viktoriia Apalkova, Sergiy Tsyganov, Nataliia Meshko
    • Project administration
      Viktoriia Apalkova, Sergiy Tsyganov
    • Resources
      Viktoriia Apalkova, Serhii Apalkov
    • Software
      Viktoriia Apalkova, Serhii Apalkov
    • Supervision
      Viktoriia Apalkova, Sergiy Tsyganov, Nataliia Meshko
    • Validation
      Viktoriia Apalkova, Nadiia Tsyganova
    • Visualization
      Viktoriia Apalkova, Serhii Apalkov
    • Writing – original draft
      Viktoriia Apalkova, Serhii Apalkov
    • Writing – review & editing
      Sergiy Tsyganov