Australian Agricultural Climate Vulnerability Index Raw Dataset

Description

Raw dataset Excel file allowing to calculate the agricultural Climate Vulnerability Index for six Australian states (New South Wales, Queensland, South Australia, Tasmania, Victoria, and Western Australia) from my master's thesis titled "Just Transition for Australian Agriculture: Assessing Climate Vulnerability and Inequalities in Australian States 1990-2024".

Steps to reproduce

1. Missing data treatment (Stata). 1.1. Treatment for Frost days: Linear Interpolation for WA: an average of 1990 and 1992 values. Last Observation Carried Forward: 2024 value is carried forward from 2023 in all states. 1.2. Treatment for Fire area burned per state size (ha): Trailing Moving Average imputation: impute with each state's 5-year trailing mean (2018-2022). 1.3. Treatment for Population density: Exclusion due to a larger number of missing values. 1.4. Treatment for Total area irrigated (ha): Exclusion due to a larger number of missing values. 1.5. Treatment for Annual production of wheat (tonnes): Trailing Moving Average imputation: impute with 3-year trailing state mean. 2. Standardisation process (Min-Max Normalisation) in Stata. The standardisation formula is applied across all states between 1990 and 2024. 3. Inversion of variables where higher values indicate better conditions in Stata. Done for Annual rainfall, Annual production of wheat, Productivity estimates, Climate adjusted productivity estimates, Farm area per state size, Farm cash income, and Farm business profit. 4. Principal Component Analysis in Stata. For each vulnerability dimension, the first principal component (PC1) is retained as the sub-index. 5. Calculation of the Climate Vulnerability Index in Stata. Formula: Climate Vulnerability Index = (Exposure + Sensitivity) – Adaptive Capacity where higher CVI value indicates higher vulnerability. 6. Theil Index calculation in Stata. 6.1. Trend test. Simple Linear Regression used to examine the changes in state inequality between 1990 and 2024, that is the relationship between the Theil Index as the dependent variable and the calendar year as the independent variable. 6.1. Regression Analysis. Panel Regression Analysis used in order to examine the impact of three independent, external regressors (that is Fire burned area per 100km2, Farm business debt, and Farm area) on the dependent variable (CVI on state-year level) across a 34-year period (1990-2024). For detailed description of the methods please refer to my master's thesis titled "Just Transition for Australian Agriculture: Assessing Climate Vulnerability and Inequalities in Australian States 1990-2024" available through the University of Vienna.

Institutions

• University of Vienna

  Vienna, Vienna

Categories

Licence