Lelo Nxumalo

Maryland has committed to cutting greenhouse gas emissions 60% by 2031 and reaching net zero by 2045 — among the most ambitious climate targets of any U.S. state. But setting a target is the easy part. The hard part is designing a carbon pricing system that actually works: one that cuts emissions, generates revenue for the clean energy transition, and protects the communities that can least afford higher energy costs. That is what this toolkit is built to do. Figure 1: Maryland total CO 2 emissions, 2000–2021 (Source: FRED) What it does The Maryland Carbon Pricing Toolkit is a set of four Python scripts that take you from raw federal data to a complete policy analysis in minutes. It pulls real emissions data from two public APIs — the Federal Reserve Economic Data (FRED) service for state-level CO 2 t...

Methodological Deep Dive Unraveling the Turkey-Germany asymmetry through the lens of trade data standards (GTS vs. STS). In bilateral trade, the math should be simple: what Country A reports as an export to Country B should theoretically match what Country B reports as an import. In practice, however, these numbers rarely align perfectly. I analyzed the trade relationship between Turkey (Reporter: Export) and Germany (Partner: Import) to quantify this "Asymmetry Gap." Using Databricks and PySpark, I processed annual trade records to distinguish between systemic reporting differences and massive, commodity-specific anomalies. Methodology Data Source UN Comtrade mont...

In global trade, "efficiency" often masks "fragility." We know that China is central to the global electronics supply chain, but how central? And if that node were to go dark, who would feel the shockwaves first? To answer this, I moved beyond standard trade statistics and built a network simulation using the OECD Inter-Country Input-Output (ICIO) Tables (2023 Edition) . This dataset maps the DNA of the global economy, tracking every dollar of input across 66 countries and 45 industries. 1. The Blast Radius: Tracing the Contagion I treated the global economy as a directed graph and simulated a total supply shock to Chinese Electronics (CHN_C26) . By tracing the flow of inputs across three tiers of buyers, I visualized the "Blast Radius" of this disruption. Fig 1: The Supply Chain Cascade. The shock originates in China (Red) and immediately hits "Tier 1" assembly hubs (Dark Blue) before cascading to global consumers...

In my previous post, we looked at where value is created in the global economy. We saw how a "German" car is actually a European collaborative effort. But that map leaves one critical question unanswered: Who owns the factory? Does a country own its own production, or is it merely a host for foreign capital? To answer this, I dove into the OECD Activity of Multinational Enterprises (AMNE) database. By distinguishing between domestic and foreign-owned firms, we can reveal the "hidden architecture" of corporate control. 1. The Sovereignty Gap: Who Relies on Foreign Capital? The first metric I calculated is the Foreign Penetration Ratio : the percentage of a nation's total Gross Value Added (GVA) that is generated by foreign-controlled affiliates. The results reveal a stark divide in the global economy between "Sovereign Giants" and "Global Hubs." Fig 1: The "Hubs" vs. The "Giants" — Sha...

We often speak of trade in simple, bilateral terms: "Germany exports cars to France" or "China ships phones to the US." But in a modern hyper-connected economy, these statements are deceptively simple. A "German" car might contain a Polish engine, Italian electronics, and Chinese batteries, while a "Chinese" phone may effectively be a bundle of Korean chips and American intellectual property wrapped in plastic. To understand who really creates value in the global economy, we cannot rely on standard customs data. We need to look deeper. The interactive network map below does exactly that, peeling back the layers of "Gross Trade" to reveal the Global Value Chains (GVCs) underneath. Global Value Chain Network (2021) Interactive Map: Hover over any arrow above to see the exact trade volume (USD Millions). Click an arrow to highlight the path. The Data Problem: The "$1,000 iPhone" Illusion ...