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on Project, Program and Portfolio Management |
| By: | Joaquin Vespignani (Tasmanian School of Business and Economics, University of Tasmania, Australia); Russell Smyth (Department of Economics, Monash University, Clayton, Australia) |
| Abstract: | This paper employs insights from earth science on the financial risk of project developments to present an economic theory of critical minerals. Our theory posits that back-ended critical mineral projects that have unaddressed technical and nontechnical barriers, such as those involving lithium and cobalt, exhibit an additional risk for investors which we term the “back-ended risk premium”. We show that the back-ended risk premium increases the cost of capital and, therefore, has the potential to reduce investment in the sector. We posit that the back-ended risk premium may also reduce the gains in productivity expected from artificial intelligence (AI) technologies in the mining sector. Progress in AI may, however, lessen the back-ended risk premium itself through shortening the duration of mining projects and the required rate of investment through reducing the associated risk. We conclude that the best way to reduce the costs associated with energy transition is for governments to invest heavily in AI mining technologies and research. |
| Keywords: | Critical Minerals, Artificial Intelligence, Risk Premium |
| JEL: | Q02 Q40 Q50 |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:mos:moswps:2024-08&r=ppm |
| By: | Ballesteros, Marife M.; Lorenzo, Pauline Joy M.; Ramos, Tatum P.; Ancheta, Jenica A.; Rodil, Amillah S. |
| Abstract: | The Philippine government has promoted and institutionalized the delivery of basic services in resettlement sites through various flagship housing programs and the issuance of policies, guidelines, and/or standards. Existing literature suggests, however, that most resettlement sites lack the basic services and the social and economic opportunities to ensure the development of liveable and sustainable communities. The study notes that resettlement projects must be carefully planned in terms of both the processes and the physical design. Government laws and policies must be translated into clear minimum standards that are adopted at the national and subnational level. To formulate these standards, the authors reviewed existing local and international policies and guidelines on resettlement housing and examined the good practices in selected resettlement projects and among project implementers. The policy mapping and case study led to the identification of policy and implementation gaps, which were used in the development and refinement of the assessment tool for resettlement planning. Comments to this paper are welcome within 60 days from the date of posting. Email publications@pids.gov.ph. |
| Keywords: | social housing;resettlement projects;settlement planning;resettlement standards |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:phd:dpaper:dp_2024-02&r=ppm |
| By: | Thomas Benninger; Dan Devlin; Eduardo Camero Godinez; Nate Vernon |
| Abstract: | Mining and petroleum projects share characteristics distinguishing them from other sectors of the economy, which has led to the use of dedicated fiscal regimes for these projects. The IMF’s Fiscal Affairs Department uses fiscal modeling to evaluate extractive industry fiscal regimes for its member countries, and trains country officials on key modeling concepts. This paper outlines important preconditions needed for effective fiscal modeling, key evaluation metrics, and emphasizes the importance of transparent modeling practices. It then examines the modeling of commonly-used fiscal instruments and highligts where their economic impact differs, and how fiscal models can inform fiscal regime design. |
| Keywords: | Natural resource taxation; extractive industries; progressivity; economic rents; fiscal modeling; investment analysis; mining; petroleum. |
| Date: | 2024–04–26 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:2024/089&r=ppm |
| By: | Xuanye Wang |
| Abstract: | We study the theoretical consequence of p-hacking on the accumulation of knowledge under the framework of mis-specified Bayesian learning. A sequence of researchers, in turn, choose projects that generate noisy information in a field. In choosing projects, researchers need to carefully balance as projects generates big information are less likely to succeed. In doing the project, a researcher p-hacks at intensity $\varepsilon$ so that the success probability of a chosen project increases (unduly) by a constant $\varepsilon$. In interpreting previous results, researcher behaves as if there is no p-hacking because the intensity $\varepsilon$ is unknown and presumably small. We show that over-incentivizing information provision leads to the failure of learning as long as $\varepsilon\neq 0$. If the incentives of information provision is properly provided, learning is correct almost surely as long as $\varepsilon$ is small. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.08984&r=ppm |
| By: | Boylan, Sally; Arsenault, Catherine; Barreto, Marcos; Bozza, Fernando A; Fonseca, Adalton; Forde, Eoghan; Hookham, Lauren; Humphreys, Georgina S; Ichihara, Maria Yury; Le doare, Kirsty; Liu, Xiao Fan; McNamara, Edel; Mugunga, Jean Claude; Oliveira, Juliane F; Ouma, Joseph; Postlethwaite, Neil; Retford, Matthew; Reyes, Luis Felipe; Morris, Andrew D; Wozencraft, Anne |
| Abstract: | The COVID-19 pandemic highlighted the importance of international data sharing and access to improve health outcomes for all. The International COVID-19 Data Alliance (ICODA) programme enabled 12 exemplar or driver projects to use existing health-related data to address major research questions relating to the pandemic, and developed data science approaches that helped each research team to overcome challenges, accelerate the data research cycle, and produce rapid insights and outputs. These approaches also sought to address inequity in data access and use, test approaches to ethical health data use, and make summary datasets and outputs accessible to a wider group of researchers. This Health Policy paper focuses on the challenges and lessons learned from ten of the ICODA driver projects, involving researchers from 19 countries and a range of health-related datasets. The ICODA programme reviewed the time taken for each project to complete stages of the health data research cycle and identified common challenges in areas such as data sharing agreements and data curation. Solutions included provision of standard data sharing templates, additional data curation expertise at an early stage, and a trusted research environment that facilitated data sharing across national boundaries and reduced risk. These approaches enabled the driver projects to rapidly produce research outputs, including publications, shared code, dashboards, and innovative resources, which can all be accessed and used by other research teams to address global health challenges. |
| Keywords: | ICODA; an initiative funded by the Gates Foundation (INV-017293); the Minderoo Foundation; supported by Microsoft’s AI for Good Research Laboratory; and convened by Health Data Research UK. Aridhia Informatics was funded by the Gates Foundation (INV-021793) |
| JEL: | C1 |
| Date: | 2024–05–01 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:122811&r=ppm |