Related Experiment Video
Updated: Aug 26, 2025
11:21
Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
Published on: July 27, 2018
8.3K
A Path Toward Systemic Equity in Life Cycle Assessment and Decision-Making: Standardizing Sociodemographic Data
Joe F Bozeman1, Erin Nobler2, Destenie Nock3
1Civil and Environmental Engineering, Public Policy, Georgia Institute of Technology, Atlanta, Georgia, USA.
Environmental Engineering Science
|October 5, 2022
Summary
This study introduces systemic equity, a framework for integrating social equity in energy and environmental research. It offers a 10-step process for developing sociodemographic data practices to ensure equitable resource, policy, and cultural needs are met.
Area of Science:
- Environmental Science
- Social Science
- Energy Policy
Background:
- Social equity is increasingly vital in energy and environmental sectors.
- Equitable sociodemographic data is crucial for UN Sustainable Development Goals.
- Existing frameworks lack consolidation for equity in life cycle assessment.
Purpose of the Study:
- To consolidate knowledge on equity applications in energy and environmental research.
- To provide examples of sociodemographic data requirements.
- To propose a framework for holistic equity administration.
Main Methods:
- Critique of existing practices.
- Development of the systemic equity framework (distributive, procedural, recognitional equity).
- Definition of ostensible, aspirational, and exploitational equity.
- Establishment of a 10-step process for sociodemographic data practices.
Main Results:
- The systemic equity framework integrates resource, policy, and cultural needs.
- Definitions for ineffective equity administration are provided.
- An adaptive 10-step process for standardizing sociodemographic data practices is presented.
Conclusions:
- The systemic equity framework and 10-step process offer a path for equitable research and practice.
- Urgent adoption by energy and environmental scientists with stakeholders is recommended.
- The framework is translatable to other research and practitioner communities.
Related Concept Videos
01:22Applications of Life Tables
105
Life tables are versatile across various fields, providing a quantitative basis for analyzing mortality and survival rates. Whether used by demographers, actuaries, epidemiologists, or sociologists, life tables offer valuable insights into the dynamics of life and death, facilitating informed decisions in public health, insurance, conservation, and beyond. Their broad applicability highlights the interconnectedness of demographic data with practical outcomes in everyday life and strategic...
105
01:29Bias in Epidemiological Studies
527
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:
527
01:25Statistical Methods for Analyzing Epidemiological Data
489
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
489
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
79
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
79
01:22Life Tables
163
A life table is a statistical tool that summarizes the mortality and survival patterns of a population, providing detailed insights into the likelihood of survival or death across different age intervals within a cohort. By organizing data on survival probabilities and mortality rates, life tables offer a clear snapshot of population dynamics over time. They are extensively used in demography, public health, actuarial science, and ecology to analyze life expectancy, design health interventions,...
163
01:20Actuarial Approach
117
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
117