@article{RamakrishnanKalkuhlAhmadetal.2020,
  author    = {Ramakrishnan, Anjali and Kalkuhl, Matthias and Ahmad, Sohail and Creutzig, Felix},
  title     = {Keeping up with the Patels},
  series = {Energy research \& social science},
  volume    = {70},
  journal   = {Energy research \& social science},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {2214-6296},
  doi       = {10.1016/j.erss.2020.101742},
  pages     = {12},
  year      = {2020},
  abstract  = {End-users base their consumption decisions not only on available budget and direct use value, but also on their social environment. The underlying social dynamics are particularly important in the case of consumer goods that implicate high future energy demand and are, hence, also key for climate mitigation. This paper investigates the impact of social factors, with a focus on 'status perceptions', on car and appliance ownerships by urban India households. Using two rounds of the household-level data from the India Human Development Survey (IHDS, 2005 and 2012), we test for the impact of social factors in addition to economic, demographic, locational, and housing on ownership levels. Starting with factor analysis to categorise appliances by their latent characteristics, we then apply the bivariate ordered probit model to identify drivers of consumption among the urban households. We find that while income and household demographics are predominant drivers of car and appliance uptake, the household's perception of status, instrumented by a variable measuring expenditure on conspicuous consumption, emerges as a key social dimension influencing the uptake. The results indicate how households identify themselves in society influences their corresponding car and appliance consumption. A deeper understanding of status-based consumption is, therefore, essential to designing better demand-side solutions to low carbon consumption.},
  language  = {en}
}
@article{PuriVardeMelo2023,
  author    = {Puri, Manish and Varde, Aparna S. and Melo, Gerard de},
  title     = {Commonsense based text mining on urban policy},
  series = {Language resources and evaluation},
  volume    = {57},
  journal   = {Language resources and evaluation},
  publisher = {Springer},
  address   = {Dordrecht [u.a.]},
  issn      = {1574-020X},
  doi       = {10.1007/s10579-022-09584-6},
  pages     = {733 -- 763},
  year      = {2023},
  abstract  = {Local laws on urban policy, i.e., ordinances directly affect our daily life in various ways (health, business etc.), yet in practice, for many citizens they remain impervious and complex. This article focuses on an approach to make urban policy more accessible and comprehensible to the general public and to government officials, while also addressing pertinent social media postings. Due to the intricacies of the natural language, ranging from complex legalese in ordinances to informal lingo in tweets, it is practical to harness human judgment here. To this end, we mine ordinances and tweets via reasoning based on commonsense knowledge so as to better account for pragmatics and semantics in the text. Ours is pioneering work in ordinance mining, and thus there is no prior labeled training data available for learning. This gap is filled by commonsense knowledge, a prudent choice in situations involving a lack of adequate training data. The ordinance mining can be beneficial to the public in fathoming policies and to officials in assessing policy effectiveness based on public reactions. This work contributes to smart governance, leveraging transparency in governing processes via public involvement. We focus significantly on ordinances contributing to smart cities, hence an important goal is to assess how well an urban region heads towards a smart city as per its policies mapping with smart city characteristics, and the corresponding public satisfaction.},
  language  = {en}
}