Diego Ponce de Leon Barido

Relationship between urbanization and CO2 emissions depends on income level and policy.

We investigate empirically how national-level CO2 emissions are affected by urbanization and environmental policy. We use statistical modeling to explore panel data on annual CO2 emissions from 80 countries for the period 1983-2005. Random- and fixed-effects models indicate that, on the global average, the urbanization-emission elasticity value is 0.95 (i.e., a 1% increase in urbanization correlates with a 0.95% increase in emissions). Several regions display a statistically significant, positive elasticity for fixed- and random-effects models: lower-income Europe, India and the Sub-Continent, Latin America, and Africa. Using two proxies for environmental policy/outcomes (ratification status for the Kyoto Protocol; the Yale Environmental Performance Index), we find that in countries with stronger environmental policy/outcomes, urbanization has a more beneficial (or, a less negative) impact on emissions. Specifically, elasticity values are -1.1 (0.21) for higher-income (lower-income) countries with strong environmental policy, versus 0.65 (1.3) for higher-income (lower-income) countries with weak environmental policies. Our finding that the urbanization-emissions elasticity may depend on the strength of a countrys environmental policy, not just marginal increases in income, is in contrast to the idea of universal urban scaling laws that can ignore local context. Most global population growth in the coming decades is expected to occur in urban areas of lower-income countries, which underscores the importance of these findings.

Information and communication technologies and climate change adaptation in Latin America and the Caribbean: a framework for action

Despite ongoing interest in deploying information and communication technologies (ICTs) for sustainable development, their use in climate change adaptation remains understudied. Based on the integration of adaptation theory and the existing literature on the use of ICTs in development, we present an analytical model for conceptualizing the contribution of existing ICTs to adaptation, and a framework for evaluating ICT success. We apply the framework to four case studies of ICTs in use for early warning systems and managing extreme events in the Latin American and the Caribbean countries. We propose that existing ICTs can support adaptation through enabling access to critical information for decision-making, coordinating actors and building social capital. ICTs also allow actors to communicate and disseminate their decision experience, thus enhancing opportunities for collective learning and continual improvements in adaptation processes. In this way, ICTs can both communicate the current and potential impacts of climate change, as well as engage populations in the development of viable adaptation strategies.

Mapping Induced Residential Demand for Electricity in Kenya

Despite substantial gains in the past few decades, 550 million people in sub-Saharan Africa still lack access to electricity. Rural areas present the largest electrification challenge, with access levels below 12% in most countries. Public rural electrification efforts, where undertaken, have generally effected slow and limited change. Further, to motivate the substantial investment required for traditional large-scale generation and transmission projects, strong demand for electricity services is required, and this demand is not easily demonstrated in rural African settings in which little data and substantial uncertainty exist. In this paper, we develop a predictive model for mapping induced residential demand for electricity -- the hypothetical demand that would exist if access to electricity services were made available. We apply this model on a fine geographic basis to Kenya to demonstrate the applicability of the approach to informing public or private electrification efforts. Together with spatially explicit cost models for generation, transmission, and distribution, these induced demand predictions can be used to evaluate various technology options, business models, and tariff structures, or to guide public sector electrification program development.

Evidence and future scenarios of a low-carbon energy transition in Central America: A case study in Nicaragua

© 2015 IOP Publishing Ltd. The global carbon emissions budget over the next decades depends critically on the choices made by fast-growing emerging economies. Few studies exist, however, that develop country-specific energy system integration insights that can inform emerging economies in this decision-making process. High spatial- and temporal-resolution power system planning is central to evaluating decarbonization scenarios, but obtaining the required data and models can be cost prohibitive, especially for researchers in low, lower-middle income economies. Here, we use Nicaragua as a case study to highlight the importance of high-resolution open access data and modeling platforms to evaluate fuel-switching strategies and their resulting cost of power under realistic technology, policy, and cost scenarios (2014-2030). Our results suggest that Nicaragua could cost-effectively achieve a low-carbon grid (≥80%, based on non-large hydro renewable energy generation) by 2030 while also pursuing multiple development objectives. Regional cooperation (balancing) enables the highest wind and solar generation (18% and 3% by 2030, respectively), at the least cost (US

Increasing Grid Flexibility Through Improved Electricity Demand Prediction in Nicaragua

127 MWh-1). Potentially risky resources (geothermal and hydropower) raise system costs but do not significantly hinder decarbonization. Oil price sensitivity scenarios suggest renewable energy to be a more cost-effective long-term investment than fuel oil, even under the assumption of prevailing cheap oil prices. Nicaraguas options illustrate the opportunities and challenges of power system decarbonization for emerging economies, and the key role that open access data and modeling platforms can play in helping develop low-carbon transition pathways.

Temperature and Humidity Dependence for Household- and City-Wide Electricity Demand Prediction in Managua, Nicaragua

Renewable energy provides an increasingly significant contribution to power production around the globe. The variable and uncertain nature of certain renewable energy sources, however, requires increased grid flexibility to reliably match electricity supply with demand. On average, wind energy accounts for 20% of Nicaraguas total generation, and can produce up to 50% within a given hour. Under the renewable energy regime fuel-oil generators are the main source of grid flexibility. Information-driven flexibility, such as improved demand prediction, can be used to reduce the need of fuel-oil based flexibility without affecting reliability. This paper evaluates and compares the use of multiple linear regression (MLR) and support vector regression (SVR) in their ability to minimize electricity demand forecast error in Nicaragua. We find SVR reduces the mean absolute percent error of prediction to 3.8%, compared with MLR (7.7%). SVR further performs a prediction with 21% less error than the current prediction mechanism employed by the utility. Finally, we discuss how improved prediction algorithms can be used to reduce Nicaraguas dependency on fuel-oil for flexibility, while also reducing costs for the utility.

Enabling Micro-level Demand-Side Grid Flexiblity in Resource Constrained Environments

Hourly electrical energy demand predictions improve grid reliability, stability, and minimize costs by maintaining system frequency and optimizing unit commitment and economic dispatch. Weather data, specifically ambient temperature and humidity, is commonly used as a predictor for demand. This paper utilizes the data from a recent demand response and behavioral energy efficiency pilot in Managua, Nicaragua in order to evaluate the relationship between household temperature and demand data, city-wide temperature and demand data, and the potential for utilizing household-level data to predict city-wide demand. Results from this paper indicate that temperature and humidity data can help to inform both household-level and city-wide prediction of electricity demand. Further, the available household level data was found to have a limited relationship with city-wide demand.

Sustainability lessons from shale development in the United States for Mexico and other emerging unconventional oil and gas developers

The increased penetration of uncertain and variable renewable energy presents various resource and operational electric grid challenges 1 . Micro-level (household and small commercial) demand-side grid flexibility could be a cost-effective strategy to integrate high penetrations of wind and solar energy, but literature and field deployments exploring the necessary information and communication technologies (ICTs) are scant. This paper presents an exploratory framework for enabling information driven grid flexibility through the Internet of Things (IoT), and a proof-of-concept wireless sensor gateway (FlexBox) to collect the necessary parameters for adequately monitoring and actuating the micro-level demand-side. In the summer of 2015, thirty sensor gateways were deployed in the city of Managua (Nicaragua) to develop a baseline for a near future small-scale demand response pilot implementation. FlexBox field data has begun shedding light on relationships between ambient temperature and load energy consumption, load and building envelope energy efficiency challenges, latency communication network challenges, and opportunities to engage existing demand-side user behavioral patterns. Information driven grid flexibility strategies present great opportunity to develop new technologies, system architectures, and implementation approaches that can easily scale across regions, incomes, and levels of development.