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## 📊 Executive Summary
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This study employs **counterfactual inference** and **interrupted time series (ITS) regression** to quantify the impact of the Sheffield Clean Air Zone implemented on February 27, 2023.
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This study employs **counterfactual inference** and **interrupted time series (ITS) regression** to quantify the impact of the Sheffield Clean Air Zone implemented on February 27, 2023. By analyzing **35,064 hourly observations** (Jan 2022 – Dec 2025) and controlling for seasonality and exogenous factors, this analysis demonstrates a statistically significant reduction in traffic-related pollution.
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By analyzing **35,064 hourly observations** (Jan 2022 – Dec 2025) and controlling for seasonality and exogenous factors, this analysis demonstrates a statistically significant reduction in traffic-related pollution.
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### 🏆 Key Findings
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**Key Results:**
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***Significance:** The intervention caused a statistically observable structural break in NO₂ levels (**p < 0.001**).
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***Magnitude:** Estimated reduction of **30%–44%** compared to the counterfactual baseline.
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***Trend:** Immediate level drop followed by a sustained downward trajectory.
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***Significant Reduction:** The CAZ intervention drove a **41% weather-normalised reduction** in NO₂ concentrations.
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***Structural Break:** Statistical testing confirms a highly significant structural break ($p < 0.001$) at the intervention date.
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***Weather Robustness:** Results hold even after accounting for wind speed, temperature, and precipitation using XGBoost.
To ensure robustness, this analysis utilizes a multi-model approach to isolate the policy effect from natural variation (weather, seasonality, and secular trends).
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To ensure robustness, this analysis utilizes a multi-model approach to isolate the policy effect from natural variation.
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### 1. Baseline Forecasting (ARIMA & Prophet)
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***Objective:** Construct a counterfactual "business as usual" baseline based on pre-intervention dynamics.
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***Specification:**`SARIMA` for linear autocorrelation and `Prophet` for complex multi-seasonal patterns.
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***Result:** Actual post-intervention NO₂ levels diverged significantly below both forecasts.
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Constructed a "business as usual" counterfactual based on pre-intervention dynamics. The **Prophet** model captured complex multi-seasonal patterns (yearly, weekly), revealing that traditional ARIMA methods may underestimate the effect size when strong seasonalities are present.
***Result:** Confirmed that the reduction was driven by emissions changes, not favorable weather.
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Used Gradient Boosting to model non-linear relationships between weather (wind, temperature) and pollution. This proved that the reduction was driven by emissions changes, not favorable weather conditions.
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### 3. Interrupted Time Series (ITS) Regression
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***Objective:** Formally test for causal structural breaks using segmented regression.
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***Model:** $Y_t = \beta_0 + \beta_1 T + \beta_2 D + \beta_3 P + \epsilon_t$
***Conclusion:** The policy resulted in both an immediate drop and an accelerated rate of improvement.
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### 4. Deep Learning Forecasting (LSTM)
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***Objective:** Capture complex non-linear temporal dependencies for high-precision forecasting.
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Formal statistical test for causal structural breaks.
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***Level Change:** Immediate drop ($\beta_2 = -2.35$, $p<0.01$).
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***Trend Change:** Accelerated rate of improvement ($\beta_3 = -0.014$, $p<0.001$).
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---
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## 💻 Reproducible Workflow
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## 🚀 How to Run the Analysis
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This repository contains a fully reproducible R pipeline designed for environmental policy auditing. The analysis flows sequentially from data extraction to final reporting.
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This repository contains a fully reproducible R pipeline. Follow these steps to replicate the study:
The dashboard below visualizes the raw time series against the intervention timeline. Note the clear shift in the data distribution post-February 2023.
The divergence between the **counterfactual forecast (dotted)** and **observed data (solid)** represents the "clean air dividend" generated by the policy.
*Data Scientist | Time Series Analysis | Policy Evaluation*
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A data science professional focused on leveraging advanced statistical modeling to solve complex real-world problems. Experienced in building reproducible analytical pipelines, causal inference, and translating data into actionable strategic insights.
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