The role of combining national official statistics with global monitoring to close the data gaps in the environmental SDGs

The Sustainable Development Goals (SDGs) have elevated the profile of the environmental dimension of development – and how we monitor this dimension. However, they have also challenged national statistical systems and the global statistical community to put in place both the methodologies and mechanisms for data collection and reporting on environmental indicators. According to a recent analysis, there is too little data to formally assess the status of 68% of the environment-related SDGs [1]. Many environment-related indicators were not part of the purview of national statistical systems and did not have a methodology or data collection system in place prior to the adoption of the SDG indicator framework [2]. Moderate improvements have been made, as evidenced by the reduced proportion of environment-related SDG indicators classified as Tier III between the original classification in 2016 and May 2019 – dropping from 50% to 28% [3]. As of March 2020, there are currently no Tier III indicators; however, as many of the SDG indicators have been recently reclassified the data availability and experience in compiling these indicators is severely limited. Socioeconomic indicators have far outpaced environmental indicators in this shift, with only 7% of non-environmental indicators classified as Tier III in May 2019 [1,4,5]. As the custodian agency for 26 of the environment-related SDG indicators, UN Environment is establishing methodologies and mechanisms to collect country-level data. However, many countries currently do not have national systems in place for monitoring these environmental indicators and thus there is a risk that much of the environmental dimension of development cannot be captured by using reporting mechanisms which only include traditionally collected national official statistics. For many of these indicators, UN Environment is exploring new data sources, such as data from citizen science. Citizen science has the potential to contribute to global and local level SDG monitoring. Realizing its full potential however, would require building key partnerships around citizen science data and creating an enabling environment. Global modelling is another approach to fill data gaps. These new types of data could not only improve global estimations but could be incorporated in national official statistics in order to improve nationally relevant data and analysis [6]. The Global Material Flow database, which estimates Domestic Material Consumption (covering SDG indicators 8.4.2 and 12.2.2), and the Global Surface Water Explorer application (covering SDG indicator 6.6.1) are a couple of examples of where UN Environment is complementing national data with global data products in the official SDG reporting process. In these cases the use of globally-derived data has been agreed by the Inter-Agency and Expert Group on SDG Indicators (IAEG-SDGs) [7]. Expanding globally-estimated or -modelled data to cover environment-related SDG indicators could build the foundation for a digital ecosystem for the planet, which would provide a basis for developing integrated analysis and insights. A Sustainability Gap Index could be one mechanism to bring together the environmental dimension of development into a single metric, which could inform the achievement of the SDGs, environmental assessments and national policy. This paper presents a summary of how the world is faring in terms of measuring the environmental dimension of the SDGs

ASCA Observations of the Composite Warm Absorber in NGC 3516

We obtained X-ray spectra of the Seyfert 1 galaxy NGC~3516 in March 1995 using ASCA. Simultaneous far-UV observations were obtained with HUT on the Astro-2 shuttle mission. The ASCA spectrum shows a lightly absorbed power law of energy index 0.78. The low energy absorbing column is significantly less than previously seen. Prominent O~vii and O~viii absorption edges are visible, but, consistent with the much lower total absorbing column, no Fe K absorption edge is detectable. A weak, narrow Fe~K$\alpha$ emission line from cold material is present as well as a broad Fe~K$\alpha$ line. These features are similar to those reported in other Seyfert 1 galaxies. A single warm absorber model provides only an imperfect description of the low energy absorption. In addition to a highly ionized absorber with ionization parameter $U = 1.66$ and a total column density of $1.4 \times 10^{22}~\rm cm^{-2}$, adding a lower ionization absorber with $U = 0.32$ and a total column of $6.9 \times 10^{21}~\rm cm^{-2}$ significantly improves the fit. The contribution of resonant line scattering to our warm absorber models limits the Doppler parameter to $< 160~\rm km~s^{-1}$ at 90\% confidence. Turbulence at the sound speed of the photoionized gas provides the best fit. None of the warm absorber models fit to the X-ray spectrum can match the observed equivalent widths of all the UV absorption lines. Accounting for the X-ray and UV absorption simultaneously requires an absorbing region with a broad range of ionization parameters and column densities.Comment: 14 pages, 4 Postscript figures, uses aaspp4.sty To appear in the August 20, 1996, issue of The Astrophysical Journa

The Joinpoint-Jump and Joinpoint-Comparability Ratio Model for Trend Analysis with Applications to Coding Changes in Health Statistics

Analysis of trends in health data collected over time can be affected by instantaneous changes in coding that cause sudden increases/decreases, or “jumps,” in data. Despite these sudden changes, the underlying continuous trends can present valuable information related to the changing risk profile of the population, the introduction of screening, new diagnostic technologies, or other causes. The joinpoint model is a well-established methodology for modeling trends over time using connected linear segments, usually on a logarithmic scale. Joinpoint models that ignore data jumps due to coding changes may produce biased estimates of trends. In this article, we introduce methods to incorporate a sudden discontinuous jump in an otherwise continuous joinpoint model. The size of the jump is either estimated directly (the Joinpoint-Jump model) or estimated using supplementary data (the Joinpoint-Comparability Ratio model). Examples using ICD-9/ICD-10 cause of death coding changes, and coding changes in the staging of cancer illustrate the use of these models

The Joinpoint-Jump and Joinpoint-Comparability Ratio Model for Trend Analysis with Applications to Coding Changes in Health Statistics

Analysis of trends in health data collected over time can be affected by instantaneous changes in coding that cause sudden increases/decreases, or “jumps,” in data. Despite these sudden changes, the underlying continuous trends can present valuable information related to the changing risk profile of the population, the introduction of screening, new diagnostic technologies, or other causes. The joinpoint model is a well-established methodology for modeling trends over time using connected linear segments, usually on a logarithmic scale. Joinpoint models that ignore data jumps due to coding changes may produce biased estimates of trends. In this article, we introduce methods to incorporate a sudden discontinuous jump in an otherwise continuous joinpoint model. The size of the jump is either estimated directly (the Joinpoint-Jump model) or estimated using supplementary data (the Joinpoint-Comparability Ratio model). Examples using ICD-9/ICD-10 cause of death coding changes, and coding changes in the staging of cancer illustrate the use of these models

Counting on the World to Act: A Roadmap for Governments to Achieve Modern Data Systems for Sustainable Development

Eradicating poverty and hunger, ensuring quality education, instituting affordable and clean energy, and more – the Sustainable Development Goals (SDGs) lay out a broad, ambitious vision for our world. But there is one common denominator that cuts across this agenda: data. Without timely, relevant, and disaggregated data, policymakers and their development partners will be unprepared to turn their promises into reality for communities worldwide. With only eleven years left to meet the goals, it is imperative that we focus on building robust, inclusive, and relevant national data systems to support the curation and promotion of better data for sustainable development. In Counting on the World to Act, TReNDS details an action plan for governments and their development partners that will enable them to help deliver the SDGs globally by 2030. Our recommendations specifically aim to empower government actors – whether they be national statisticians, chief data scientists, chief data officers, ministers of planning, or others concerned with evidence in support of sustainable development – to advocate for, build, and lead a new data ecosystem

The Type Ic Supernova 1994I in M51: Detection of Helium and Spectral Evolution

We present a series of spectra of SN 1994I in M51, starting 1 week prior to maximum brightness. The nebular phase began about 2 months after the explosion; together with the rapid decline of the optical light, this suggests that the ejected mass was small. Although lines of He I in the optical region are weak or absent, consistent with the Type Ic classification, we detect strong He I λ10830 absorption during the first month past maximum. Thus, if SN 1994I is a typical Type Ic supernova, the atmospheres of these objects cannot be completely devoid of helium. The emission-line widths are smaller than predicted by the model of Nomoto and coworkers, in which the iron core of a low-mass carbon-oxygen star collapses. They are, however, larger than in Type Ib supernovae

Intergalactic UV Background Radiation Field

We have performed proximity effect analysis of low and high resolution data, considering detailed frequency and redshift dependence of the AGN spectra processed through galactic and intergalactic material. We show that such a background flux, calculated using the observed distribution of AGNs, falls short of the value required by the proximity effect analysis by a factor of $\ge$ 2.7. We have studied the uncertainty in the value of the required flux due to its dependence on the resolution, description of column density distribution, systemic redshifts of QSOs etc. We conclude that in view of these uncertainties the proximity effect is consistent with the background contributed by the observed AGNs and that the hypothesized presence of an additional, dust extinct, population of AGNs may not be necessary.Comment: To be published in the Journal of Astronomy and Astrophysics aasms, 2 figures, 2 tables. Paper replaced to include the figure