Resource Stock Analysis 1 Last Updated: August 02, 2018; First Released: July 15, 2014 Author: Kevin Boyle, President, DevTreks (1*) Version: 2.1.4 A. Introduction This reference explains how to start to collect, measure, and analyze basic resource stock data (2*). DevTreks believes that all resource stock data, from the carbon budgets of Iowan corn fields to student performance indicators for El Salvadoran students, has stories to tell and lessons to teach. Those lessons can only be learned when data about resource stocks is collected, measured, aggregated, analyzed, explained, and saved in online knowledge banks. Full, uniform, and accurate analyses of per capita C02 emission balances for Beijing firms, food quality ratings for Bangladeshi street vendors, nutrient management budgets for Guatemalan milpa fields, knowledge balance reports for Calexico students, and health care status indicators for Ghana’s health sector, should be one or two links away for everyone. If a resource stock expert, business owner, government official, worker, parent, or nonprofit member, needs to make a decision involving resource stocks, they should have ready access to the best data and advice available. This reference introduces another DevTreks way to build these types of knowledge banks. Section Page Mitigation and Adaptation Analysis 2 Conservation Technology Assessments 13 Base Elements 18 Data URLs 20 Multipliers 21 Analyzers 22 Related Analyses 26 Knowledge Banks and Conclusions 27 Consequential Digital Activism 30 Appendix A. Multipliers 33 Appendix B. Resource Stock Analyzers 44 B. Resource Stock Imbalance Mitigation and Adaptation Analysis The IPCC WG2 and WG3, Risky Business (2014), U.S. Global Climate Change Research Program (2014), and World Bank (2014) references make it clear that natural resource stock imbalances, particularly Green House Gas (GHG) emissions that cause climate change, have serious consequences on society. Floods will increase, food security will decrease, forest fires will increase, and oceans will rise. The references make the case that moderate expenditure spent today on stock imbalance mitigation and adaptation can save future generations very large expenditures. The National Research Council (2015) concludes their evaluation of climate change interventions with the following admonition: “society must take advantage as soon as possible of [climate change interventions] that can help avoid the worst effects of warming. We will lose the opportunity if society delays in research and development to lower the technical barriers to efficacy and affordability of [climate change interventions]”. In other words, we have a moral responsibility to take action now. The analyses explained in this reference may help to provide evidence for spending the needed resources wisely. The following images demonstrate the types of economic analyses that can be explained using stock budgets and production function data. In effect, the physical science stock balances are tied to economic measures by quantifying damages avoided, reducing costs, measuring health benefit or lives saved, analyzing changes in output productivity, and similar techniques that add prices to the Inputs and/or Outputs. V. Meyer et al (2013) provide a comprehensive review of many techniques that are appropriate for estimating costs and benefits for climate change-induced disasters. Benefit Cost Analysis (EPA, 2010) Marginal Damage and Marginal Benefit Analysis (EPA, 2010) Adaptation Cost Benefit Analysis (CAP-NET, UNDP, 2009) Climate Change Costs and Adaptation Costs Analysis (IPCC, WG2) Damage, or Disaster Risk Reduction, Assessment (V. Meyer, 2013) Tradeoff Analysis (IPCC) Tradeoff Analysis (see the Social Budgeting and DevPacks tutorials) Cost Effectiveness Analysis (IPCC, WG3) Cost-Effectiveness or Marginal Abatement Cost Analysis (World Bank, 2014) The graphics demonstrate that formal economic analysis frameworks for measuring stock imbalances include Benefit Cost Analysis, Cost Effectiveness Analysis, Climate Change and Adaptation Cost Analysis, Tradeoff Analysis, and Damage Assessment (3*). Many economists have used these analyses to conclude that one of the most effective and efficient ways to reduce GHG is to put a price on carbon. For example, a carbon tax on gasoline will cause drivers to drive less and to use more fuel efficient vehicles. That point should be remembered, and remembered well, regardless of the frameworks, algorithms, and analyses, which follow. The IPPC WG2 and WG3 references (2014) demonstrate that the adaptation to, and mitigation of, resource stock imbalances can be analyzed using more than just economics. They have chapters devoted to analytic frameworks for risk, social capital, institutional capital, equity, and sustainable development. The following image (Groffman et al, 2014) demonstrates that field-oriented Adaptation Planning and Implementation Frameworks can be used for natural resources stock improvement. The next section introduces a comprehensive framework for analyzing resource stocks. C. Conservation Technology Assessments (CTAs) The following images (WHO, 2011) show that the health care sector uses the term, Health Technology Assessment (HTA), as an encompassing framework for assessing the socioeconomic impacts of health care improvement technologies, such as medical devices, on human health states (i.e. human capital stock flows and balances –refer to the SPA3 reference for a more formal definition). HTAs make extensive use of the meta-analysis of randomized control trial data. The HTA approach has international acceptance and support. Many countries (Denmark, England, Canada, Australia, France, Germany), and international organizations such as WHO, provide extensive guidance explaining how to carry out HTAs. Examples of completed HTAs can be found throughout the scientific health care literature. Governments throughout the world save substantial money and lives by heeding their results. Medical advisory groups use their results to advise doctors to stop using some procedures and start using others. For example, the New York Times reported on July 1st, 2014, that the American College of Physicians conducted an (HTA) analysis of routine pelvic examinations that caused them to advise doctors to stop carrying out the procedure because no scientific evidence supports its efficacy. The IPCC WG2 use the term Climate Impact, Adaptation, and Vulnerability, or CIAV assessment, as a framework for assessing technology adoption decisions. The IPCC WG3 appears to use the term Integrated Assessment for a similar purpose. The US Global Change Research Group (Moss et al, 2014) use terms such as Risk Assessment, Comparative Tradeoff Method, Scenario Planning, Scientific Assessment, and Sustained Assessment, as decision support frameworks supporting climate change interventions. The Resource Stock Calculation 1 reference points out that “technology assessments” are similar to the “product systems” analyzed during Life Cycle Assessments. But none of these terms are used by the natural resources conservation sector as ubiquitously as HTAs are used in the health care sector. They are also a lot more confusing. DevTreks adapts the HTA approach to resource stock analysis by introducing the term Conservation Technology Assessment (CTA). CTA is defined as: Conservation Technology Assessment is the analysis of resource stock flows and balances, and conservation technologies that are designed to prevent or correct imbalances in the stocks. The simplest goal of CTA is to promote conservation technologies that are cost effective and to demote conservation technologies that are not cost effective. The following image (WHO 2011) demonstrates that CTAs can have far more comprehensive goals. CTA provides one term that can encompass related terms such as CIAV assessment, Integrated Assessment, technology assessment, mitigation and adaptation assessment, Life Cycle Assessment, Risk Assessment, Sustained Assessment, and all of the economic assessment terms used in the previous section. In addition, the term CTA is general enough to encompass technology assessments involving human capital (i.e. HTAs), social capital, institutional capital, and cultural capital. Although the linkages between local conservation technology and global environmental impacts (carbon cycle, hydrologic cycle) complicates CTAs, an encompassing framework for studying technology and communicating the results can have the same usefulness in the conservation field as they are having in the health care field. For example, in the United States, the US Global Change Research Group (Jacoby et al, 2014) specifically identify the need for a “consistent framework for assessing [climate change mitigation interventions]”. They (Bierbaum et al, 2014) also point out “the effectiveness of climate change adaptation has seldom been evaluated, because actions have only recently been initiated and comprehensive evaluation metrics do not yet exist”. In addition, they identify the need for “methodologies to evaluate [the costs and benefits of adaptation to climate change], and … a central and streamlined database of adaptation [technologies]”. Finally, they (Correl et al, 2014) identify the need for “improved characterization of uncertainty … development of [more and better] indicators, development of [tools that measure the economic consequences of climate change] … development of new tools that [identify the best technologies for adaption] … [and see Footnote 5*]. The HTA literature proves that HTAs can’t be seriously completed without studying the risk and uncertainty associated with human resource stock data. The IPCC literature makes clear that natural resource stocks can’t be seriously studied without communicating changes in their amount in terms of likelihoods. Similarly, CTAs must also incorporate measures of risk and uncertainty, similar to the following IPCC WG2 “solution space”. The CTA and the Monitoring and Evaluation references contain examples demonstrating how to carry out basic resource stock risk analyses. The CTA definition uses the term Conservation in a general microeconomic sense –firms, households, and governments can improve lives and livelihoods by allocating scare resources well. The term Technology is also used in a general way –management practices, projects, and policies can also be assessed using CTAs. D. Base Elements The following image (refer to the Monitoring and Evaluation tutorials) illustrates that agricultural and health care technology improvement projects can be assessed using base elements that include Inputs, Processes, Outputs, Outcomes, and Impacts. The following table demonstrates corresponding base elements used in DevTreks to measure and analyze resource stocks: Base Elements Stock Budgets Input Operation or Component Output Outcome Time Period or Budget Residue Harvest N runoff % Farmers adoption Change in Water Quality Index Native seed Wildlife Habitat Land Prep Habitat production index Habitat restoration Change in T&E species count Orange Conventional Nutrient Management Nitrate emission Orange Crop Production Change in Eutrophication Potential Equivalents Fossil Fuel Combustion CO2 emission Climate Change Change in CO2 Level The Inputs and Outputs in these Stock Budgets are linked to generic Stock Indicators that track resource stock flows (i.e. CO2 or NH4 emission debits and credits), resource stock balances (i.e. the last column of the previous image), and the impacts of the stock flows and balances (i.e. their environmental consequences –have flood intensities increased?). The remaining elements aggregate the Input and Output Indicators with the Time Period element balancing the stocks (i.e. subtracting Output Stock Indicators from Input Stock Indicators). The Stock Indicator data can derive from field measurements, simulated model results, statistical model results, literature reviews, sensing devices, expert opinion, or hypothetical scenarios. The introductory reference discusses the management of resource stock data (i.e. bulk uploads, Data URLs holding TEXT datasets). The software can be used to supply the basic resource stock accounting that supports very basic CTA analysis, including economic analysis. Basic analysis can still support basically sound decisions. For example, the associated CTA-Prevention (CTAP) reference includes examples of CTAPs that demonstrate how to avoid double counting both the value of resource stock flows (i.e. present value of lost rental income or lost social benefits) and the damage to the resource stock itself (i.e. the market value of the asset, or the reconstruction costs of the asset –see the World Bank and UN, 2010, reference). In addition, the software can use the mathematical, statistical, and URL properties of calculators and analyzers to emulate some types of domain-specific models, such as crop nutrient budgeting models, to make basic forecasts, and to carry out more advanced decision making, such as policy evaluations. Future releases will include examples. E. Data URLs (4*) The introductory reference explains the resource stock calculations and data that will be analyzed in this reference. Analyses measure up to 15 basic, generic resource stock indicators. All of the analyses displayed in this reference is sample data that demonstrates how to collect and analyze each type of dataset. The Resource Stock Analyzers and sample data demonstrated in this reference can be found at the following URLs. These examples focus on analyzing data –most of the datasets do not have multimedia support. The Resource Stock Calculation reference introduces the upgraded Version 2.1.4 patterns for using TEXT datasets to run the underlying calculations. Analyzers URI: https://www.devtreks.org/greentreks/select/carbon/linkedviewgroup/Stock Analyzers/64/none/ Inputs URI: https://www.devtreks.org/greentreks/preview/carbon/input/2014 Fertilizer, Orange, Conventional/2147397531/none/ Outputs URI: https://www.devtreks.org/greentreks/preview/carbon/output/2014 Orange, Conventional LCA/2141223454/none/ Components URI: https://www.devtreks.org/greentreks/preview/carbon/componentgroup/LCA Organic Orange Crop Components/660/none/ Operations URI: https://www.devtreks.org/greentreks/preview/carbon/operationgroup/LCA Conventional Orange Crop Operations/760/none/ Outcomes URI: https://www.devtreks.org/greentreks/preview/carbon/outcomegroup/LCA Conventional Orange Crop Outcomes/39/none/ Capital Budgets URI: https://www.devtreks.org/greentreks/preview/carbon/investment/Conventional Orange Budget/428/none/ Operating Budgets URI: https://www.devtreks.org/greentreks/preview/carbon/budgetgroup/LCA Organic vs Conventional Orange Crops/2140761977/none/ DevPacks URI: https://www.devtreks.org/greentreks/preview/carbon/devpackgroup/RCT Emissions and Env Performance/48/none Multimedia URI: https://www.devtreks.org/greentreks/preview/carbon/resourcepack/Life Cycle Analysis Reference/1534/none/ Story URI: https://www.devtreks.org/greentreks/preview/carbon/linkedviewpack/Resource Stock Analysis 1/180/none/ F. Multipliers (6*) The final totals generated by Resource Stock calculators can be further adjusted using base element multipliers. These multipliers include Input/Output Amounts and Times, Output Composition Amount, Operation/Component/Outcome Amounts, and Time Period Amounts. Appendix A explains how these multipliers work. G. Analyzers Resource stock Totals, Statistics, Change, and Progress, Analyzers are available for examining Inputs, Outputs, Operations, Components, Outcomes, Operating Budgets, and Capital Budgets. Standard aggregators (Label, Group Id, and Type Id) can be used to aggregate the data being analyzed (see the Calculators and Analyzers tutorial). When the data being analyzed is observational data stored in Data URL datasets, these analyzers produce automated metadata analysis (analysis of analyses). Metadata analysis of randomized control trial (RCT) data is the primary technique employed in Health Technology Assessments. The CTA reference and Appendix C, DevPacks Stock Analysis, begin to demonstrate how to carry out basic metadata analysis of randomized experimental data. The following image shows that, with the exception of Totals Analyses, up to 10 stock indicators can be chosen to analyze. The 10 stock indicator limit is only imposed for indicators that use data intensive algorithms, such as those that use Data URL datasets. Algorithms that use correlated indicator calculations always include the correlated indicators. Indicators should be entered in the base Input and Output calculators in the same order and up to 10 label-dependent Input indicators and Output indicators will be analyzed. The 10 indicator limit can be found throughout DevTreks because the display of more indicators makes the data difficult to understand. The number of observations shown in the results of analyses reflect the number of indicators being aggregated, not the number of base elements being aggregated. Indicators are aggregated based on their Indicator.Label property In the case of Time Period elements in budgets, the number of observations is based on the number of Input and Output indicators with the same labels. The properties of indicators determine whether they credit or debit a stock. The Totals Analyzer displays summations for all the quantitative Stock Indicators (Q1, Q2, Q3, Q4, Q5, QT, QTM, QTU, QTL, Score, ScoreD1, ScoreD2, ScoreM, ScoreL, and ScoreU). The remaining analyzers only aggregate the QTM, ScoreM, ScoreL, and ScoreU properties. The introductory reference demonstrates that, for many calculations, the QTM and ScoreM amounts reflect mean amounts and the ScoreL and ScoreU reflect Score.ConfidenceIntervals around the mean. If the number of observations listed in an analysis appears wrong, the reason can often be traced to inaccurate labels (i.e. N20A vs N2OA). The Totals Analysis can be used to spot the errant Indicators. The following image makes the point that the 10 stock indicators being studied should be consistent throughout the analyses being conducted. The label, Target Type, and Alternative Type, properties chosen for Operation, Component, and Outcome, Analysis may not be consistent with how those properties are used in Operating and Capital Budgets. For example, a Progress Analysis that compares two budgets needs all of budget 1 Target Types set to benchmark and budget 2 Target Types set to actual. Theses analyzers first order aggregated base elements by Id, and then Date, prior to running any analysis. That convention explains the order of the base elements that are displayed. It also explains how the algorithms used with these analyzers (as of 1.8.8) order their elements. Appendix B explains how the Totals, Statistics, Change, and Progress Analyzers work. H. Net Present Value (NPV) Calculation and Analysis With the exception of Input and Output Analyses, NPV calculators must be run prior to running these analyzers. DevTreks convention is to use NPV calculators to pull fresh database data together prior to running analyses. The NPV calculators do not rerun resource stock calculations. The NPV results support the economic analysis dimension of CTAs. The associated references contain examples demonstrating how to use Stock Indicators to calculate the uncertainty of costs and benefits. I. Performance Analysis Stock budgeting data can be used to carry out other types of Performance Analysis, such as the amount of energy per dollar income, CO2 emissions per unit energy, and environmental damage per unit CO2. The IPCC WG1 2013 reference describes how Net Primary Productivity crop studies, that make use of water and nutrient budgeting techniques, will play increasingly important roles in future efforts to adapt to climate change impacts on crop productivity (and decreased pollution from runoff). The Performance Analysis 1 tutorial explains additional types of performance measurement. The Social Performance Analysis tutorial demonstrates algorithms that begin to measure social performance (7*). J. Custom Analysis (7*) This reference explains how to analyze base element resource stock data. The structure of base element data may not support many types of analyses, such as CTAs that use randomized control trial data or comparisons of base elements that are not siblings. Three options for custom analysis can be used to overcome this shortfall: 1. DevPacks: The DevPacks tutorial introduces base elements that allow analysis of arbitrary structures of hierarchical data, including randomized control trial data and non-sibling base elements. Appendix C, DevPacks Stock Analysis begins to demonstrate how to use them to analyze resource stock data (10*). 2. Data URLs: The introductory reference introduces the Data URL properties of calculators and analyzers that allow custom datasets to be linked directly to calculators and analyzers. 3. Packages: The Calculators and Analyzers and Performance Analysis references explain that datasets can be packaged, downloaded as zip files, cleaned up, imported into statistical packages, and further analyzed. The Conservation Technology Assessment tutorial explains these techniques further. Future releases and references will also explain these techniques further. K. Knowledge Bank Standards All resource stock data should be entered into online knowledge banks (i.e. production servers as contrasted to development servers) that can be used to analyze resource stocks. That structured evidence must be passed down to future generations. These knowledge banks aggregate and analyze all of the data in a network. Future references will discuss how these knowledge banks will evolve (i.e. semantic data, forecasts) to support future decision making needs. The flexibility offered by DevTreks in documenting resource stocks means that networks need to develop “rules” explaining the “standards” that should be followed by clubs in their network. The “standards” make it possible to build knowledge banks. The IPCC and FAO references provide guidance for natural resource stock data standards. The U.S. CMS and WHO are developing human capital stock data standards that can support health care knowledge banks. Summary and Conclusions Clubs using DevTreks can start to carry out the basic analysis of resource stocks. Clubs can solicit help understanding resource stocks better and share structured evidence explaining the best technologies and practices for managing resource stocks. Networks can build knowledge banks that explain the management of resource stocks and pass that knowledge down to future generations. The result may be Indian smallholders who adapt effectively to changing monsoons, Somalian health care administrators who deliver more cost effective medical treatments, Philippine city managers who mitigate rising typhoon intensity more efficiently, California vegetable growers who manage water wisely, Egyptian municipalities that manage energy distribution better, Chilean mariners who adopt sustainable fish stock management practices faster, and people who improve their lives and livelihoods. Footnotes 1. While working as an agricultural economist for the USDA, Natural Resources Conservation Service, the author trained conservation professionals in conservation planning. These training sessions included guidance about the principles of natural resource conservation stock budgeting that underlie the natural resource examples used in this reference. In 1996, while working for the same group, he built a desktop software program, The Community Conservation Toolbox, which automated much of that agency’s conservation planning (and that provided another interesting case study about the hurdles faced in technology adoption and diffusion, or the power of inertia). 2. Analysts have developed a large number of techniques and tools for analyzing resource stocks (i.e. the climate change models in the IPCC references). This reference introduces basic stock budgeting analysis. Additional techniques will be included in future releases. 3. The natural resources references make it clear that stocks can’t be seriously analyzed without considering the uncertainty, or riskiness, of their measurement. The introductory reference includes examples showing how to conduct a basic stock risk analysis. The Conservation Technology Assessment tutorial provides additional examples of risk analysis. Future releases of the Performance Analysis tutorial will include additional examples that demonstrate how to measure risk and environmental performance. 4. A small, limited amount of natural resources conservation data was used to test the analyzers in this reference. In addition, not every feasible way to run an analysis was tested (i.e. 44 stock calculators and analyzers have to be tested on three platforms). These analyzers will continue to be tested with additional datasets in future upgrades. 5. Although significant portions of Chapters 26, 27, 28, 29, 30, and Appendix 6, in the U.S. Global Climate Change Research Program (USGCCRP) references might sound like prescriptions for CTAs, the key difference from our perspective is that these efforts (and funding) should not be limited to research organizations. The author began this project while working as an official US scientist for the USDA. He disagrees with the USGCCRP researchers’ primary recommendations to mainly use research institutions to develop and deliver the needed climate change data services. Two reasons stand out: a. Professional analysts can deliver professional CTA data services: The researchers don’t appear to recognize that information technology will eventually automate most of what they currently do by hand (and much of which, by modern IT standards, is obsolescent). The assistance of anyone who has the skills, or can be trained, to complete a professional CTA is needed and should be sought. Some CTAs will require completion by researchers, but many analyses, such as cost benefit analyses of new mitigation technologies, can be completed by professional analysts. b. Professional information technologists can develop and deliver professional CTA data services: Given the importance of information, information technologists may need to be hiring researchers as consultants, rather than vice versa. Many very, very talented software developers do not work in research organizations but may be highly motivated to assist with the tremendous amount of software development needed to deliver all of the missing climate change data services. These developers should not end up suspecting that a narrow cabal of research peers are their main beneficiary –they will work to help their neighbors, communities, and future generations, but not so that a researcher can get another paper published (unless the researchers pay them enough money to do so). Although this difference in opinion is a potential reason why researchers may ignore the obvious (i.e. this technology exists, albeit imperfectly, and like most new technology companies, outside conventional institutions), the same researchers make the convincing case that the planet really does need all of the help it can get. 6. The IPCC (2006) and FAO (2014) references use the term Activity Data (AD) in a manner similar to these multipliers. They define AD as “Activity Data describe the magnitude of human activity resulting in emissions or removals of greenhouse gases, taking place during a given period of time and over a specified period”. This number is multiplied by an emissions factor, which is taken from an IPCC reference, to calculate total emissions or removals. National statistics agencies use this technique to build inventories of emissions data. The Resource Stock Calculation reference demonstrates one way to build these types of inventories and to account for their uncertainty. 7. Version 2.1.6 began investigating the development of lighter weight versions of DevTreks. For example, storing TEXT datasets in document databases, and then using a general metadata user interface for running the calculations and analyses. This alternative software design may be more appropriate for the purposes introduced in the Social Performance Analysis references. Specifically to rate the social soundness of companies (i.e. consumers scan bar codes before making purchases or use the app to develop their shopping lists) and public executives (i.e. voters inspect the ratings before making decisions). This design may be a logical complement to the existing relational database design, but consumer-oriented software for conducting consequential digital activism may be more appropriate for better funded and networked organizations (i.e. but then again). 8. The IPCC WG 1 reference makes extensive use of “scenarios” in explaining the impacts of climate change. They define a scenario as “A plausible description of how the future may develop based on a coherent and internally consistent set of assumptions about the key driving forces (e.g. rate of technological change, prices) and relationships. Note that scenarios are neither predictions nor forecasts, but are useful to provide a view of the implications of developments and actions.” In DevTreks parlance, a base or benchmark comparator is a base scenario from which alternative scenarios are compared (i.e. using Change by Alternative analyses). The same reference uses the terms “time trend” and “inventory trend” in a manner consistent with Change by Year analyses. 9. The IPCC WG 1 reference defines a storyline as “A narrative description of a scenario (or family of scenarios), highlighting the main scenario characteristics, relationships between key driving forces and the dynamics of their evolution”. 10. DevPacks Analyses are somewhat harder to carry out than standard analyses. Consider using them when resources are particularly scarce, money really does need to be saved, and evidence has to be easily accessible that proves outcomes. A casual glance at newspaper stories suggests these circumstances seldom happen in some countries (and households, and firms, and agencies, and research organizations, and villages, and cities, and counties, and states) –in many cases, accountability for budget expenditures is mostly a talking point. UN GAR (2015) suggests that “[by holding the parties responsible for the decisions they make about the investments in their communities] the subsequent losses and impacts will become a societal issue that can be subject to societal discussion and negotiation”. 11. These types of analyses are based on the general principle “is it better to give a community another ‘expert’ analysis, or is it better to give them the tools needed to build the analyses and make the decisions themselves about consequent courses of action?”. 12. The metadata analysis of RCT data conducted in HTAs has more complexity (and richness) than these examples of basic CTA analyses. Basic RCT analysis is still a good place to start. References The references used in this tutorial can be found in the introductory reference, Resource Stock Calculation 1. References Note We try to use references that are open access or that do not charge fees. Improvements, Errors, and New Features Please notify DevTreks (devtrekkers@gmail.com) if you find errors in these references. Also please let us know about suggested improvements or recommended new features. A video tutorial explaining this reference can be found at: https://www.devtreks.org/commontreks/preview/commons/resourcepack/Resource Stock Analysis 1/1525/none/ Video Errata: The video used earlier versions of the Stock Calculators and Analyzers. They’ve matured a lot since this video was made. The World Bank publication endorsed by Nobel Prize winners is actually: World Bank and United Nations. Natural hazards, unnatural disasters: the economics of effective prevention. 2010 Appendix A. Multipliers The following examples use an Operating Budget Analysis of an organic orange crop to explain how multipliers work. Multipliers work similarly to the Indicators that are aggregated in a Totals Analysis. Aggregate indicators sum all Q1s, Q2s, Q3s, Q4s, Q5s, QTs, and Scores that have the same label. Multipliers are then applied to these aggregated Qxs. Analysts are expected to interpret the aggregations and summations appropriately. Oftentimes, the only properties that are important, and used by the Statistical, Change, and Progress Analyzers, are the QTM, ScoreM, ScoreL, and ScoreU properties. The IPCC 2066 reference points out that activity data, which is equivalent to these multipliers, is often collected in a way that already accounts for its uncertainty (i.e. census data). Under these circumstances, base element multipliers can be used as the activity data. As an alternative to base element multipliers, the associated CTA tutorial explains how uncertain multiplier indicators can be used in the calculations. The following image (Version 1.7.6) displays the initial fertilizer Input example explained in the introductory reference. The Input and Output stock indicators were calculated as “unit stocks” (i.e. 1 kg fertilizer/ha and 1 ton yield/ha). The Resource Stock Input and Output calculators have been run but no multipliers have been used yet with these calculations. The multipliers are only used with calculated results, meaning that Math Expressions can’t be used with the multiplied variables in the Expression (but can be used with QTs and Scores). Although not shown, the Output N indicator QTM equals 1.5047. The following image shows that when the Input.OCAmount property is changed from 1 to 100 (kg N/ha applied), all of the final indicator Qx properties increase proportionately. Note that most analyses are primarily interested in the QT and Score properties (final emissions and environmental performances). Also note that the Nitrate Indicator uses a triangular distribution with a monte carlo sampling algorithm –the numbers change slightly each time a new calculation is run because the same sample is not used with each calculation (i.e. the Score.RandomSeed = 0). Operations (and Operating Budgets) multiply the stock indicators by the Input.OCAmount and the Input.Times properties. Components (and Capital Budgets) multiply the stock indicators by the Input.CAPAmount and the Input.Times properties. Under some circumstances it may be appropriate to use the Input.AOHAmount to allocate resource indicators to overhead, but this allocation is not currently supported. Although not shown, the Output.Amount property was also changed from 1 ton/ha to 100 ton/ha (for ease of computation) and the Output indicator properties increased by that factor (QTM = 150). Outcomes (and both Operating Budgets and Capital Budgets) multiply the stock indicators by the Output.Amount, Output.CompositionAmount, and Output.Times properties. The following image shows that when the Input.Times property is changed from 1 to 2, all of the Input and Operation properties double. Although not shown, the Output.CompositionAmount property was also changed from 1 to 2 and the the Output.Times property was also changed from 1 to 2. The Output indicator properties quadrupled (QTM = 603). The following image shows that when the Operation.Amount property is changed from 1 to 2, the Input and Operation indicator properties double. Although not shown, the Outcome.Amount property was also changed from 1 to 2 resulting in the Output indicator properties doubling (QTM = 1195). The following before and after images show that when the TimePeriod.Amount property is changed from 1 to 2, the Input and Operation properties don’t change but the TimePeriod Input and Output indicator properties all double (QTM = 2,401). Again, the before-aggregated TimePeriod multipliers are used. Before After Appendix B. Resource Stock Analyzers a. Totals Analyses A Totals Analysis sums all stock indicators with the same labels for every base element in an analysis. All analyzers run this analysis for each aggregated base element before carrying out additional calculations. Version 2.0.4 stopped rerunning Resource Stock Input or Output calculations prior to summing the calculations because the original base element calculations aren’t changed during analyses. Appendix A explains how to use multipliers to adjust the original base element calculations. The analyzers associated with these calculators, explained in the introductory reference, sum the base Input and Output Stock Indicators into their ancestor Operations, Components, Outcomes, Time Periods, and Budgets. In addition, they are multiplied by the multipliers (i.e. Amounts) found in those elements (see Appendix A. Multipliers). In this regard, they behave the same as benefit and cost data which gets cumulatively summed and multiplied throughout budgets. In fact, they were designed in this manner to better support cost and benefit data analysis techniques such as cost effectiveness analysis. The following Input Totals Analysis shows that summations of Input Series have been aggregated into a parent Input. In this example of a Life Cycle Analysis of orange production, emissions are being aggregated into emission indicators with the same label, and environmental impacts are being aggregated into impact indicators with the same label. The Observations reflect the number of indicators being aggregated. The first indicator being aggregated sets the Name, Description, Labels, Units, and Mathematical properties. The following stylized Operating Budget Analysis demonstrates that no Output indicators have the same label as an Input indicator, so their full amounts appear in the Time Period totals (N and P). This life cycle analysis wanted full information about emissions and environmental impacts, rather than just balances (i.e. N balance), so it used different labels for the Output indicators. The Totals analysis displays up to 15 aggregated Input and Output indicators. The USEPA reference (2006) includes an example of a LCA for auto emissions that includes dozens of stock indicators. Use more than one Input or Output when more than 15 indicators are needed. The analysis is stylized because Version 1.7.8 upgraded just enough indicators to test the calculations. b. Statistics Analyses A Statistics Analysis uses the Totals calculations to measure basic statistical properties of up to 10 aggregated resource stock indicator properties. Total, Median, Mean, Variance, and Standard Deviation statistics are generated for all of the stock indicators in aggregated base elements. Indicators are aggregated in two stages. The first stage uses the standard aggregators to aggregate the base elements. The second stage aggregates the same resource stock indicators within each aggregated base element. The number of observations reflects the number of individual indicators being aggregated. The following Input Statistical Analysis shows that the digital precision displayed by analyzers is 4 digits. The digital precision of analyses is important because this type of emissions and environmental impact data is often aggregated across regions or populations. Some of the analytic results for very small amounts show no numeric changes, yet they still have percentage changes. The 4 digit precision doesn’t display the amount change, but still displays the correct percent change. If warranted, the digital precision may be increased in future releases. The following Operating Budget Statistical Analysis demonstrates that the resource stock composition of Outputs and Inputs can be included in an analysis by setting their labels differently. Alternatively, the balance of the stocks will be shown at the Time Period element if the Input and Output indicators have the same labels. c. Change Analyses Change Analyzers can examine incremental changes in 10 resource stock totals or statistical means. A Change by Year Analysis measures incremental changes between aggregated stock indicators that have different Years. A Change by Id Analysis measures incremental changes between stock indicators that have different Ids. A Change by Alternative Type Analysis measures incremental changes between aggregated stock indicators that have different Alternative Types. Changes are analyzed in ascending order (Id = 1,2,3; Year = 2000, 2001, 2002; AlternativeType = A, B, C). The first member of the sequence will be used as a “Base” element to make comparisons. The sibling sequence member immediately before the current sequence member will be used as an “x-1” (x minus 1) element to make comparisons. Gaps in the sequence, such as a missing Year, will be ignored. Stock budgeting analysis often use a “base scenario” as the most important comparator in an analysis (8*). Particular attention must be paid to define the base comparators in these types of analyses. Further details about how Change Analyzers work can be found in the Change Analysis tutorial. [As of 1.9.2 the following feature is being retained, but not debugged, until more advanced RCT algorithms are developed that can either replace, or enhance, this feature. The following image demonstrates that Version 1.8.8 expanded the properties of Change Analyzers to include Math Type, Math Sub Type, Math Expression, Confidence Interval, and Math Result, properties. These properties were added to leverage the algorithms used in Resource Stock Calculators. More specifically, they were added to support RCT analysis, a key technique used in HTAs and CTAs. Note how they compare statistical means, rather than totals. The CTA reference and Appendix C, DevPacks Stock Analysis begin to demonstrate how to use these properties to carry out basic metadata analysis of RCT data. An important convention to remember when using this type of analysis is how base elements are ordered –first by Id and then by Date.] The following three year Input Change by Year Analysis tracks annual changes in stock indicators. Under what circumstance might stock indicators change? Double check Score High Amount in this image. The following Capital Budget Change by Year Analysis measures incremental changes in resource stock characteristics for three years of budgets. Year 2014 is being compared to Year 2013 and Year 2012. Capital Budgets can be used to analyze durable resource stocks, while Operating Budgets can be used to analyze expendable resource stocks. Agricultural crops are normally analyzed using Operating Budgets. Building construction is normally analyzed using Capital Budgets. The Ag Production and Building Construction Tutorials demonstrate how to analyze both stock types using either of the two budgets. The following Operating Budget Change by Alternative Analysis measures incremental changes in resource stock characteristics for two alternative budgets. What may be the significance of having this type of data for every complementary cropping system in the world? Other than institutional factors, is there any technical reason why such data can’t be collected? The Time Periods in the analysis line up because of the way both Alternative Type and Label properties were set. Time Periods begin to use aggregate Stock indicators (Input and Outputs, with the properties of indicators determining whether they credit or debit the stock). The Outcomes and Operations line up because of the way both Alternative Type and Label properties were set. d. Progress 1 Analyses A Progress 1 Analysis uses the Totals calculations to measure actual versus planned progress for up to 10 aggregated resource stock indicator properties. Base elements that have a Target Type property set to “benchmark” act as a comparator for base elements using a Target Type property set to “actual”. Only base elements with the same label are analyzed. Stock budgeting analysis often use a “benchmark scenario” as the most important comparator in an analysis (7*). Particular attention must be paid to define the benchmark comparators in this type of analysis. Further details about how Progress Analyzers work can be found in the Earned Value Management Analysis tutorial. The following three year Input Analysis shows that the benchmark comparator is a 2012 Input. Both the 2013 and 2014 actual Inputs are being compared to the 2012 comparator. The following three year Operation Analysis compares a 2012 Operation benchmark comparator to 2013 and 2014 actual Operations. Note that most progress analyses have the same number of planned and actual base elements (which cause the planned analytic properties to be comparable to the actual analytic properties). Although these stocks are related to agriculture, what if the stocks were related to human capital -what may be the significance of having student resource stock indicator data for every school in the world, or health care status indicators for every hospital? Other than institutional factors, is there any technical reason why such data can’t be collected? The following three year Capital Budget Analysis compares a 2012 benchmark comparator with 2013 and 2014 actual Budgets. All three Time Periods are being compared because they have the same Labels and the 2012 Time Period has a Target Type = “benchmark” while 2013 and 2014 have Target Types = “actual”. Note that most real Progress analyses include a second planned year (that’s why planned progress numbers don’t change). The online Operating Budget Analysis, found using the Operating Budget URI, compares progress using a benchmark Budget 1 with 3 Time Periods and an actual Budget 2 with 3 Time Periods. At the Budget Level of this analysis, the cumulative progress of actual Budget 2 are displayed. The Time Periods in the analysis line up because of the way both Target Type and Label properties were set. At the Time Period Level of this analysis, actual Budget 2’s Time Periods are being compared to benchmark Budget 1’s Time Periods (2012 Time Period Labels = A1002012, 2013 Time Period Labels = A1002013 …). The Outcomes and Operations in the analysis line up because of the way both Target Type and Label properties were set. At the Outcome and Operations Level of this analysis, actual Budget 2’s Outcomes are being compared to benchmark Budget 1’s Outcomes (Budget 1, 2012 Time Period OC 1 Label = A1001012, Budget 2, 2012 Time Period OC 1 Label = A1001012 …). In fact, on localhost the 2013 and 2014 Outcomes and Operations don’t line up because their Target Type properties were set up to test the Operation and Outcome Analyzers. The order of the two Investments matter. The benchmark’s Ids have to come before the actual. They should also appear in the search engine before the actual. Appendix C. DevPacks Stock Analysis (11*) DevPacks support the analysis of arbitrary hierarchies of data, such as randomized control trial data. They also support a variety of custom analyses, such as the comparisons of base elements that are not siblings. When the data being analyzed is observational resource stock data stored in Data URL TEXT files, the resultant metadata analysis can provide scalable and powerful decision support. This appendix contains examples demonstrating different ways to use DevPacks to conduct Resource Stock Analysis. The DevPacks tutorial should be read prior to this appendix. Example 1. Change by Alternative (non-sibling Operating Budget base element analysis) URL https://www.devtreks.org/greentreks/preview/carbon/devpackgroup/RCT Emissions and Env Performance/48/none 3 Sibling Orange Conventional The following image shows that an Operating Budget data service has several different children Budget Groups. The Groups named A, B, and C, have been organized by some expert logic, such as geographic region, technology type, biophysical factors, or the need to comply with a professional WBS. In this analysis, they have been organized to demonstrate how to compare non-sibling budgets. The budgets in Budget Group A are not siblings of Budget Group B or Budget Group C. How can budgets from the latter groups be compared to budgets in A? The following image shows that 3 sibling DevPack Part base elements have been built to hold the budgets needing comparison. The following steps are taken to compare the stock characteristics of the 3 non-sibling budgets. These steps should first be followed for a small sample of the total data being analyzed because several iterations will be needed before the steps are carried out correctly. Although this example uses Operating Budgets, the same steps will work for all base element analyses, including Inputs, Outputs, Operations, Components, Outcomes, and Capital Budgets (but full tests are still needed). 1. Set Correct Properties for an Analysis: The properties of each budget being compared was checked for accuracy. A Change by Alternative Analysis is needed, meaning their AlternativeType properties had to be set to A, B, and C, particularly at the Budget, Time Period, Operation, and Outcome elements. In addition, the Label property of each comparable base element in the budgets had to match because that property is used to determine which base elements to compare. The Budget, Time Period, Outcome, and Operation, Labels had to be the same for each respective base element in each budget. If these properties have already been set to carry out existing analyses, they should not be changed –instead Step 4 should be used to edit the budgets. 2. Run New NPV Calculations: After all corrections were made, new NPV calculations were run for each budget and saved. The NPV calculations had been inserted in the children Time Periods and their AlternativeType properties were double-checked to ensure they had the correct A, B, and C properties. The resultant analyses will be stored in an xml file. That file is the file that will be copied to a DevPack Part. This particular analysis is primarily interested in the Stock analysis, not the NPV totals, so they do not represent a real orange crop budget. 3. Download Packages: The 3 budgets needing comparisons were packaged separately and downloaded separately. The DevPacks tutorial explains how to find the calculated NPV file. Often, the NPV xml file will be the file with the greatest size. 4. Make Final Edits: The downloaded budgets were double checked to make sure they could be compared. In this case, the AlternativeType properties of each budget were checked to make sure that the 3 budgets had respective A, B, and C values for each AlternativeType property. One change had to be made in budget 2 and budget 3. The Labels had been set correctly. This is also the correct time to change any property needed for any purpose, such as random control trial adjustments (of prices, output amounts, input amounts). Oftentimes, randomized control trial budgets contain 95% or more of the same properties –existing budgets can be copied and edited for each observation in the trial. 5. Upload Files to DevPack Parts: Each budget was uploaded to a corresponding DevPack Part. 6. Run DevPack Part NPV Calculators (optional): NPV calculators were linked to each DevPack Part and each calculator was run and saved. The resultant files become the base documents that will be further analyzed. They contain all of the linked views for all of the base elements within budgets, including their associated multipliers. In this analysis, the most important linked views are the Input and Output stock calculations. Version 1.8.8 made this an optional step –if good NPV documents have already been uploaded to the DevPackPart, the analyzer will use those to run the analysis. 7. Run DevPack Resource Stock Analyzers: The parent DevPack is used to compare the 3 budgets. Resource Stock Totals and Change by Alternative Analyzers were linked to the parent DevPack (NPV calculators are only used with the DevPack Parts). The analyses were run and saved. The Resource Stock Analyzers should coincide with the base elements being compared (if Inputs are being compared, choose Input Stock Analyzers rather than Operating Budget Stock Analyzers). The following images show the resultant Change by Alternative Stock Analysis. This analysis was carried out by combining the 3 budgets into 1 file and then analyzing the aggregated file. This technique will not scale to handle thousands of files (yet). An existing, scalable technique, is to use budgets that contain meta-data analysis of Data URL TEXT datasets. Future releases will address scalability further. Example 2. Change by Id with Algorithm (Input or Output base element randomized data) (12*) URL https://www.devtreks.org/greentreks/preview/carbon/devpackgroup/RCT Emissions and Env Performance/48/none 3 Sibling Input Stocks This was not a priority for version 1.9.2+ and has not been debugged. The previous example demonstrated that optional NPV calculations were run for Operating Budgets (or Operations, Components, Outcomes, and Capital Budgets) after uploading them to DevPackParts. Inputs and Outputs don’t use NPV calculators. This example demonstrates that the base element documents uploaded into a DevPackPart, can be used directly, without requiring any additional DevPackPart calculation. In this example, those base documents are the files generated from running Input Stock Calculators. If needed, the files can be edited in the usual manner (by hand) prior to being uploaded. If needed, they can also be recalculated in the DevPackPart (the files that are analyzed always look for files with the newest calculations). Version 2.0.4 stopped running stock calculations during analyses, so care must be taken to ensure the latter calculation is current. The base elements that will be analyzed are the same 3 Input Series used with Example 1ma in the CTA reference. Each series member had an Input Stock calculator run and saved. The calculations derive from random experimental data stored using Data URL TEXT files. This example will analyze the aggregated calculations using Example 1m’s Analysis of Variance (ANOVA) algorithm (algorithm1, subalgorithm8). The following image shows that an Input Resource Stock Change by Id Analysis has been run for the DevPack containing the 3 DevPackParts. This DevPack analysis is identical to the results demonstrated for Example 1ma, Method 3, in the CTA reference. The following image confirms that the comparative analysis of this Resource Stock data is also the same as Example 1m of the CTA reference. This analysis was carried out by combining the 3 input documents into 1 file and then analyzing the aggregated file. This technique will not scale to handle thousands of files or input/output series (yet). An existing, scalable technique, is to use Inputs and Outputs that contain meta-data analysis of Data URL TEXT datasets. Future releases will address scalability further. As a side point, this analysis ran incorrectly until all aggregated base elements used in Stock Analyzers were reordered first by Id and then Date (a point to remember when analyzing non-sibling base elements). Example 3. Change by Id with Algorithm (Operation base element randomized data) URL https://www.devtreks.org/greentreks/preview/carbon/devpackgroup/RCT Emissions and Env Performance/48/none Operation Stock Algo This was not a priority for version 1.9.2+ and has not been debugged. This example adds the same Inputs used in Example 2 to three Operation base elements, runs NPV calculations for each of the Operations, copies the resultant calculated document to a DevPackPart, and runs the same ANOVA algorithm. The following image displays the Change by Id analyzer settings used in the analysis. The following image demonstrates that this analysis generates the same confidence intervals as shown in Example 2. Although not tested yet, this technique should work for all base elements, including Outcomes, Components, Operating Budgets, and Capital Budgets. DevTreks –social budgeting that improves lives and livelihoods 1