KIPA Project – Waste Price Determinants Analysis

Goal

The goal of this project is to identify correlations and patterns between waste prices and potential price determinants such as weather, energy, and business cycle factors.

The wPreis Dataset

• Price of waste from around Sep 2020 until Sep 2023
• Could be negative (client received payment for waste) or positive (client paid for waste disposal)
• 10 unique clusters (collection of Postleitzahl) with 4 unique product categories
• No null values
• Correlations observed within categories in the same cluster

Exploring Potential Price Determinants

Weather - First Approach

• Data extracted from an open meteo free API
• Parameters include temperature at 2 meters, wind speed at 10 meters, precipitation, rain, and snowfall
• Accessed using latitude and longitude of cities in each cluster

Weather - Second Approach

• Data obtained from Deutscher Wetterdienst (DWD), the German Meteorological Service
• Provides weather data dating back to the 1830s
• Parameters include temperature mean and max

Energy - Electricity

• Data from netztransparenz.de (ct/kWh) for the whole of Germany from Jan 2021

Energy - Oil

• Global Oil and Gas Market Prices from Yahoo Finance used as proxy data
• Adjusted Close Price (Adj Close) considered for analysis

Energy - Gas

• Gas prices collected from Yahoo Finance for the whole of Germany
• Exploration of correlation with Adjusted Close Price (Adj Close)

Business Cycle - DAX

• DAX (Deutscher Aktien Index) data obtained from Yahoo Finance
• Calculated the adjusted close price of the weekly average for correlation analysis

Construction

• Data on construction permits number (per land per month) taken from Statistik der Baugenehmigungen (code 31111)

Conclusions

• Weather determinants show very low correlation with waste prices
• Oil exhibits a significant correlation, around 0.6
• Gas shows lesser correlation than oil
• Electricity shows some high correlations, especially for certain lags
• Business Cycle (DAX) shows some significant inverse correlation
• Construction permits show high inverse correlation for some clusters

Recommendations

• Avoid weather as a determinant due to low correlation
• Consider oil as a determinant but be mindful of major global events
• Further explore electricity as a determinant, especially for lags
• Consider DAX and Construction permits, noting the inverse correlation
• Avoid gas as a determinant due to lower correlation