An original dataset that covers 1) details and 2) ownership of the largest GHG-emitting industrial assets in Pakistan.
Background: Pakistan’s GHG greenhouse gas (GHG) emissions come from point sources, such as coal plants, and more diffuse sources such as livestock. Two sectors account for 87% of all GHG emissions: energy and agriculture. The energy sector contributes 46% of Pakistan's total annual GHG emissions, of which 26% is attributed to electricity consumption, 25% to manufacturing, 23% to transportation, and the remaining 25% to other energy subsectors. Agriculture accounts for 41% of total GHG emissions, of which enteric fermentation (livestock digestion) is the primary contributor (46%).
This research sets out to create an original dataset of GHG assets in the country for analytical purposes so that IGC researchers may better understand the domestic politics of climate mitigation.
1. Collecting and entering online data.
The RA will be required to use publicly available sources to fill a database on Power Plants, Manufacturing and Infrastructure. Many of these are publicly available through the World Bank, Government of Pakistan and other databases.
Power plants: Make a table of all of the power plants with crucial details (installed megawatts, type, district, fuel type, combustion type, tariff, ultimate owner).
Manufacturing: Make a table of the 10 largest manufacturing facilities by revenue that use their own generation of energy using fossil fuels (coal, gas etc).
Transportation: Make a table of the largest vehicle firms with their current details and ultimate ownership
Agriculture: Make a table of the 10 largest firms by revenue in the livestock business, for animals such as cows and sheep (ignore chickens, etc.).
2. Call and visit industry and government for missing data.
This requires familiarity with navigating bureaucracy, engaging with ministers and mid-level bureaucracy.
3. Link these data to ownership.
Compile a table of the firms that own the assets in parts 1-4 with basic details like HQ location, and any obvious linkages to politics, army, etc.
Review asset declarations: Use a proprietary data sheets for previous MP/Senator asset declarations to clean and review data. The researcher will review the data for any assets that correspond to the categories (power plants, manufacturing, transportation, and livestock). Where a link is found, the researcher shall link the data.
Role of the Research Assistant
Much of the required information is available from publicly available sources. In some cases, particularly in livestock and manufacturing, the researcher will be required to reach out to industry bodies and firms to collect some information. For this reason, we have allocated significantly more time for these sectors on a per-item basis. Sources (phone call, website, etc.) should be listed in an allocated column in the database.
What to flag:
We need the researcher to flag any information which they have a low level of certainty so that we can make a determination as to whether to include said data in the final analysis. We are also very interested in where ownership is consolidated (between sectors) and particularly where there is a link to politics - such as a linked family member.
The supervisor will provide templates for the spreadsheets
The supervisor will provide templates for the spreadsheets so that the researchers don’t need to design those from scratch.
Working days: 41 (extension possible)
Deadline to Submit Deliverables: 19 December 2023 (first draft, flexible)
Payment: Payment of 50% will be made to the researcher for the first draft (on or before 20 November) with the balance provided upon acceptance of the final draft (50%).
● First draft on or before 20 December (50% of Payment).
● Final draft (50% of Payment)
● Master’s degree in economics, environment, energy, social sciences, or agriculture, from LUMS or equivalent.
● Demonstrated interest in climate, energy, or environmental issues.
● Excellent ability to work independently, conduct empirical research, and attention to detail.
● Fluency in English and Urdu.