Development of a Multi-Region Input-Output Database for Policy Applications

Countries face different problems depending on factors such as geographical position, climate, wealth, political regime, and natural resources. Given this diversity, it is important that economic, social, and environmental assessments utilise regionally detailed and comprehensive information. However, when examining a particular type of assessment, studies (in most cases) are usually conducted without any regional or sectoral specificity due to the difficulty of creating an inter-regional modelling framework at sub-national levels. A fundamental tool for identifying specific economic characteristics of regions (either global or within a nation) is a multi-region input-output (MRIO) system. Through the understanding of regional economic distribution, sectoral contribution, and inter-regional supply chain network, input-output (I-O) based assessments are capable of providing a comprehensive picture of regional economic structures. However, the creation of an MRIO system is a time-consuming task that requires skill in handling the complexity of data compilation and reconciliation. To this end, finding an alternative method for creating an MRIO database in the most efficient way is necessary. In this thesis, I developed new MRIO databases that utilised virtual laboratory technology: IndoLab, TaiwanLab, SwedenLab, and USLab , and also took part in developing the JapanLab. I then demonstrated the use of these new facilities for addressing research questions surrounding employment multipliers in Indonesia, economic impacts due to natural disasters in Taiwan, regional consumer emissions in Sweden, and the responsibility for food loss in Japan. In addition, I presented the application of a new dataset in the global MRIO database for assessing the carbon footprints of global tourism sectors

Global socio-economic losses and environmental gains from the Coronavirus pandemic

On 3 April 2020, the Director-General of the WHO stated: “[COVID-19] is much more than a health crisis. We are all aware of the profound social and economic consequences of the pandemic (WHO, 2020)”. Such consequences are the result of counter-measures such as lockdowns, and world-wide reductions in production and consumption, amplified by cascading impacts through international supply chains. Using a global multi-regional macro-economic model, we capture direct and indirect spill-over effects in terms of social and economic losses, as well as environmental effects of the pandemic. Based on information as of May 2020, we show that global consumption losses amount to 3.8$tr, triggering significant job (147 million full-time equivalent) and income (2.1$tr) losses. Global atmospheric emissions are reduced by 2.5Gt of greenhouse gases, 0.6Mt of PM2.5, and 5.1Mt of SO2 and NOx. While Asia, Europe and the USA have been the most directly impacted regions, and transport and tourism the immediately hit sectors, the indirect effects transmitted along international supply chains are being felt across the entire world economy. These ripple effects highlight the intrinsic link between socio-economic and environmental dimensions, and emphasise the challenge of addressing unsustainable global patterns. How humanity reacts to this crisis will define the post-pandemic world

Correction: The carbon footprint of global tourism (Nature Climate Change (2018) DOI: 10.1038/s41558-018-0141-x)

In the version of this Article originally published, in the penultimate paragraph of the section "Gas species and supply chains", in the sentence "In this assessment, the contribution of air travel emissions amounts to 20% (0.9 GtCO2e) of tourism's global carbon footprint." the values should have read "12% (0.55 GtCO2e)"; this error has now been corrected, and Supplementary Table 9 has been amended to clarify this change

The carbon footprint of global tourism

Tourism contributes significantly to global gross domestic product, and is forecast to grow at an annual 4%, thus outpacing many other economic sectors. However, global carbon emissions related to tourism are currently not well quantified. Here, we quantify tourism-related global carbon flows between 160 countries, and their carbon footprints under origin and destination accounting perspectives. We find that, between 2009 and 2013, tourism’s global carbon footprint has increased from 3.9 to 4.5 GtCOe, four times more than previously estimated, accounting for about 8% of global greenhouse gas emissions. Transport, shopping and food are significant contributors. The majority of this footprint is exerted by and in high-income countries. The rapid increase in tourism demand is effectively outstripping the decarbonization of tourism-related technology. We project that, due to its high carbon intensity and continuing growth, tourism will constitute a growing part of the world’s greenhouse gas emissions

Forecasting Indonesian Tax Revenue: A Case of Import Duties : IPB University, Ministry of Finance of Republic of Indonesian

Bea masuk merupakan salah satu komponen penerimaan pajak Indonesia yang terkena dampak signifikan akibat COVID-19. Kementerian Keuangan Republik Indonesia menyebutkan bahwa bea masuk mengalami kontraksi sebesar 13.5% pada tahun 2020, dan kemudian cenderung mengalami peningkatan pada tahun 2021. Dinamika yang cenderung volatile semacam itu tentu akan berpengaruh terhadap penentuan prioritas anggaran yang dilakukan oleh Kementerian Keuangan Republik Indonesia. Namun demikian, studi yang terkait dengan penerimaan bea masuk masih sangat terbatas dan sulit ditemukan. Oleh karena itu, studi ini menganalisa penerimaan bea masuk Indonesia selama periode 2016Q1-2021Q2 dan membuat model peramalan dengan menggunakan data realisasi penerimaan bea masuk bulanan. Hasil analisa menunjukkan bahwa model Auto Regressive Distributed Lag (ARDL) memiliki performa yang cukup baik untuk meramalkan bea masuk, dengan nilai MAPE pada in sample forecast sebesar 1.57%.  Bea masuk merupakan salah satu komponen penerimaan pajak Indonesia yang terkena dampak signifikan akibat COVID-19. Kementerian Keuangan Republik Indonesia menyebutkan bahwa bea masuk mengalami kontraksi sebesar 13.5% pada tahun 2020, dan kemudian cenderung mengalami peningkatan pada tahun 2021. Dinamika yang cenderung volatile semacam itu tentu akan berpengaruh terhadap penentuan prioritas anggaran yang dilakukan oleh Kementerian Keuangan Republik Indonesia. Namun demikian, studi yang terkait dengan penerimaan bea masuk masih sangat terbatas dan sulit ditemukan. Oleh karena itu, studi ini menganalisa penerimaan bea masuk Indonesia selama periode 2016Q1-2021Q2 dan membuat model peramalan dengan menggunakan data realisasi penerimaan bea masuk bulanan. Hasil analisa menunjukkan bahwa model Auto Regressive Distributed Lag (ARDL) memiliki performa yang cukup baik untuk meramalkan bea masuk, dengan nilai MAPE pada in sample forecast sebesar 1.57%

Using virtual laboratories for disaster analysis – a case study of Taiwan

Due to its geographic location, Taiwan frequently experiences severe natural disasters (for example earthquakes and typhoons) that significantly interrupt business operations and subsequently cause extensive financial losses. Prior work on economic losses resulting from such natural disasters in Taiwan has not considered regional and sectoral spillover effects. In this work, we estimate the economic impacts resulting from the 1999 Chichi earthquake, the 2009 typhoon Morakot, the 2016 Tainan earthquake, and the 2016 typhoon Megi. We do so in the new TaiwanLab, a collaborative virtual laboratory that is capable of generating a time-series of subnational multiregional input–output (MRIO) tables, capturing interregional transactions among 267 sectors across Taiwan’s 22 city-counties. We identify critical economic sectors in regions of high vulnerability to natural disasters. Our research is, thus, a credible reference to decision-making that determines regional and sectoral prioritisation for damage mitigation, improved resiliency, and faster recovery schedules

Peramalan Penerimaan Pajak Indonesia: Studi Kasus Bea Masuk: Institut Pertanian Bogor, Kementerian Keuangan Republik Indonesia

Bea masuk merupakan salah satu komponen penerimaan pajak Indonesia yang terkena dampak signifikan akibat COVID-19. Kementerian Keuangan Republik Indonesia menyebutkan bahwa bea masuk mengalami kontraksi sebesar 13.5% pada tahun 2020, dan kemudian cenderung mengalami peningkatan pada tahun 2021. Dinamika yang cenderung volatile semacam itu tentu akan berpengaruh terhadap penentuan prioritas anggaran yang dilakukan oleh Kementerian Keuangan Republik Indonesia. Namun demikian, studi yang terkait dengan penerimaan bea masuk masih sangat terbatas dan sulit ditemukan. Oleh karena itu, studi ini menganalisa penerimaan bea masuk Indonesia selama periode 2016Q1-2021Q2 dan membuat model peramalan dengan menggunakan data realisasi penerimaan bea masuk bulanan. Hasil analisa menunjukkan bahwa model Auto Regressive Distributed Lag (ARDL) memiliki performa yang cukup baik untuk meramalkan bea masuk, dengan nilai MAPE pada in sample forecast sebesar 1.57%