AGGREGATION VERSUS DISAGGREGATION IN INPUT–OUTPUT ANALYSIS OF THE ENVIRONMENT

By Manfred Lenzen. 2011

327 citations recorded by the source corpus.

Subfields: Environmental Engineering, Management, Monitoring, Policy and Law

View Paper: https://www.semanticscholar.org/paper/a8b9feaa2cf956d2a4a9d5b05688c0dbac5bb842

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Environmental Engineering

1. Manfred Lenzen
2. D. Guan
3. Richard Wood
4. G. P. Peters
5. Edgar G. Hertwich
6. Klaus Hubacek
7. T. Wiedmann
8. E. Dietzenbacher

In Management, Monitoring, Policy and Law

1. G. P. Peters
2. Edgar G. Hertwich
3. Bin Su
4. D. Guan
5. Klaus Hubacek
6. Kuishuang Feng
7. Richard Wood
8. E. Dietzenbacher

Citations in the corpus

• GLOBAL MULTIREGIONAL INPUT–OUTPUT FRAMEWORKS: AN INTRODUCTION AND OUTLOOK (2013, background): “nl 1 The projects discussed in this special issue are also portrayed in Murray and Lenzen (2013), a forthcoming popular-scientific book on MRIO.”
• BUILDING EORA: A GLOBAL MULTI-REGION INPUT–OUTPUT DATABASE AT HIGH COUNTRY AND SECTOR RESOLUTION (2013, background): “This is true even if detailed data points are few, and weights for disaggregating do not exist (Lenzen 2011), hence our decision for a disaggregated classification for all countries.”
• A synthesis of carbon in international trade (2012, background): “Nevertheless, the available literature suggests the variation in economic data may not be that important for consumption-based estimates at the national level, but differences may persist at the sector level (Lenzen et al., 2010; Lenzen, 2011; Wilting, 2012).”
• China's Energy Consumption in the New Normal (2018, background): “It needs to be noted that there are uncertainties in SDA analyses because of interaction terms (Lenzen, 2006; Wood & Lenzen, 2006) and sector aggregation (Lenzen, 2011; Lenzen et al., 2004; Su & Ang, 2010).”
• A study on embodied carbon transfer at the provincial level of China from a social network perspective (2019, background): “In addition, the compiled MRIO table can inherit a list of uncertainties in source 24 data, aggregation, allocation and balancing (Su and Ang, 2010; Lenzen, 2011; Liu and Wang, 2017).”
• Household carbon inequality in the U.S. (2021, background): “However, the Eora MRIO table only includes 26 economic sectors which is more aggregated than the US IO table, thus may lead to aggregation errors (Lenzen, 2011).”
• Revisiting drivers of energy intensity in China during 1997–2007: A structural decomposition analysis (2014, background): “A higher level of sector disaggregation is to be preferred when data availability is not an issue, which can retain full information of data and enhance policy decisions at sectoral scale (Lenzen, 2011; Su et al., 2010).”
• STRUCTURAL DECOMPOSITION ANALYSIS APPLIED TO ENERGY AND EMISSIONS: AGGREGATION ISSUES (2012, background): “They point out that a sufficiently high level of sector disaggregation should be used, a view supported by Lenzen (2011).”
• EFFECTS OF SECTOR AGGREGATION ON CO2 MULTIPLIERS IN MULTIREGIONAL INPUT–OUTPUT ANALYSES (2014, background): “• Lenzen (2011) showed that disaggregation of IO data is preferable to aggregation of environmental extension data, even if based on only a few data points.”
• THE CONSTRUCTION OF WORLD INPUT–OUTPUT TABLES IN THE WIOD PROJECT (2013, background): “Lenzen (2011) provides evidence from Monte Carlo simulations that disaggregation – even on the basis of very incomplete data – is likely to produce more accurate multiplier values.”
• DISAGGREGATING INPUT–OUTPUT MODELS WITH INCOMPLETE INFORMATION (2012, background): “Lenzen (2011) gave the example of the rice and wheat sectors being combined into one grain-growing sector.”