The global cadastre

The article discusses whether a globally connected cadastre is possible. Most land transactions occur in domestic, national land markets. However, many parties are now looking beyond their borders. Indeed, international land trading is burgeoning: governments, businesses and citizens from various countries, whether rich or poor, are now actively engaged as buyers and sellers in global land deals. Basically, it is easier to transact in the global market than ever before: land is increasingly a global commodity. The world's interconnected financial markets support this growing level of international trade and investment but, as one saw with financial markets in 2008, the quality of these global systems should not be taken for granted. Such foreign investment in land is not new: international companies have been investing for some time in commercial development, housing and mineral exploration, and more recently agriculture too

Electricity availability: A precondition for faster economic growth?

We investigate if greater electricity availability helps countries ascend to faster economic growth trajectories. This is an important question for many developing countries that are currently prioritizing infrastructure investments. Using cross-sectional and panel regressions with national-level decadal data, we find some evidence that electricity availability has a significant effect on subsequent economic growth. However, much of the effect disappears once suitable controls are included. We examine various dimensions of electricity availability, including electricity consumption quantity, generation capacity, residential access rate, and quality of electricity supply. It appears that electricity availability is best viewed as something that can be scaled up as economies grow rather than something that imposes binding constraints on subsequent economic growth.Funding was received from the Australian Research Council (DE160100750). This research was also supported by an Australian Government Research Training Program (RTP) Scholarship

Adoption of solar and wind energy: The roles of carbon pricing and aggregate policy support

This paper analyzes the roles of policies and preferences in national adoption of solar and wind energy technologies. We use cross-sectional and panel regressions for both the European Union and a broader international sample. We find that countries that price carbon emissions have gone on to adopt more solar and wind energy. The aggregate level of policy support, measured in euros per megawatt hour, appears to have been important for solar energy adoption. We also find that solar energy adoption has been larger in countries with higher proportions of people concerned about climate change. In addition, we assess the effects of other key explanators including financial system size and income levels.Australian Research Council (DE160100750); Department of Foreign Affairs and Trade through the Australian-German Energy Transition Hub; Australian Government Research Training Program (RTP) Scholarshi

How was your day? Online visual workspace summaries using incremental clustering in topic space

Someday mobile robots will operate continually. Day after day, they will be in receipt of a never ending stream of images. In anticipation of this, this paper is about having a mobile robot generate apt and compact summaries of its life experience. We consider a robot moving around its environment both revisiting and exploring, accruing images as it goes. We describe how we can choose a subset of images to summarise the robot's cumulative visual experience. Moreover we show how to do this such that the time cost of generating an summary is largely independent of the total number of images processed. No one day is harder to summarise than any other.Micro Autonomous Consortium Systems and Technology (United States. Army Research Laboratory (Grant W911NF-08-2-0004))United States. Office of Naval Research. Multidisciplinary University Research Initiative (Grant N00014-09-1-1051)United States. Office of Naval Research. Multidisciplinary University Research Initiative (Grant N00014-09-1-1031

Fast Empirical Scenarios

We seek to extract a small number of representative scenarios from large and high-dimensional panel data that are consistent with sample moments. Among two novel algorithms, the first identifies scenarios that have not been observed before, and comes with a scenario-based representation of covariance matrices. The second proposal picks important data points from states of the world that have already realized, and are consistent with higher-order sample moment information. Both algorithms are efficient to compute, and lend themselves to consistent scenario-based modeling and high-dimensional numerical integration. Extensive numerical benchmarking studies and an application in portfolio optimization favor the proposed algorithms.Comment: 22 pages, 7 figure

Efficient Grounding of Abstract Spatial Concepts for Natural Language Interaction with Robot Manipulators

Our goal is to develop models that allow a robot to understand natural language instructions in the context of its world representation. Contemporary models learn possible correspondences between parsed instructions and candidate groundings that include objects, regions and motion constraints. However, these models cannot reason about abstract concepts expressed in an instruction like, “pick up the middle block in the row of five blocks”. In this work, we introduce a probabilistic model that incorporates an expressive space of abstract spatial concepts as well as notions of cardinality and ordinality. The graph is structured according to the parse structure of language and introduces a factorisation over abstract concepts correlated with concrete constituents. Inference in the model is posed as an approximate search procedure that leverages partitioning of the joint in terms of concrete and abstract factors. The algorithm first estimates a set of probable concrete constituents that constrains the search procedure to a reduced space of abstract concepts, pruning away improbable portions of the exponentiallylarge search space. Empirical evaluation demonstrates accurate grounding of abstract concepts embedded in complex natural language instructions commanding a robot manipulator. The proposed inference method leads to significant efficiency gains compared to the baseline, with minimal trade-off in accuracy.United States. Army Research Laboratory. Robotics Consortium (Collaborative Technology Alliance Program)National Science Foundation (U.S.) (Grant No.1427547

Exploring Dynamic Capabilities, Digital Business Transformation and Indonesia’s Creative Industry Sector

Organisations that are less digitally capable are more vulnerable to the impacts of change. It is argued that organisations must enhance Dynamic Capabilities (DCs) and embrace Digital Business Transformation (DBT). However, whilst technology disruption and global competition impact both developed, and developing countries, most theories and implementations of DBT are derived from experiences in developed countries. Implementation of DBT in less developed countries requires an understanding of the current status of DBT, influencing factors and what adaptations are needed, in those contexts. The paper presents the conceptual foundations for ongoing research into how concepts of DCs and DBT are applicable to Indonesia’s Creative Industry Sector (CIS). The work has particular importance for Indonesia’s economy. Specifically, the paper provides a conceptual foundation for further research synthesized from extant DCs and DBT models. This current research suggests the importance of investigating how to bring theories of DCs and DBT into developing countries